{"as_of":"2026-08-16T02:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6beecad80dcde10d0d8b5fcf0fa303f00a844dc787eb101f28c75a44903b7cf5","coverage":[{"denominator":300,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:39:14.547983Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.06563/citation-record","integrity":"/paper/2608.06563/integrity","json":"/paper/2608.06563/citation-record.json","paper":"/paper/2608.06563"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.239948Z","title":"Deep learning with differential privacy","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.239948Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:222cb13bb0f7f8c17aaf16f7ba54a9d027ed9f330a0ff7958a7fe01c7bf2da78","observation_id":"3822d646-3a0f-42eb-84b6-32daca8bb002","resolution":{"observed_at":"2026-08-15T14:39:14.239948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.244276Z","title":"Federated learning in edge computing: a systematic survey.Sensors, 22(2):450, 2022.(Cited on page 24)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.244276Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:260178624d809bbb8764a15a20c34a56e5666a746a71c7e891bdd23119d6b93b","observation_id":"600cd291-0063-4844-9da8-36b1fd188b10","resolution":{"observed_at":"2026-08-15T14:39:14.244276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.247870Z","title":"Distributed delayed stochastic optimiza- tion.Advances in neural information processing systems, 24, 2011.(Cited on page 350)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.247870Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d7cd19cacf5d56881a1486d3b48416ea4b930004fb3247e68e69efacc7e7ad3c","observation_id":"273a8dbd-da51-42e0-a1dc-56abce94b229","resolution":{"observed_at":"2026-08-15T14:39:14.247870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.13255","last_updated":"2020-12-22T07:42:30Z","snapshot_observed_at":"2026-08-14T01:48:39.766084Z","submitted_at":"2020-12-22T07:42:30Z","title":"Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.13255","snapshot_observed_at":"2026-08-15T14:39:14.251062Z","title":"Intrinsic dimen- sionality explains the effectiveness of language model fine-tuning.arXiv preprint arXiv:2012.13255, 2020.(Cited on page 131)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.251062Z"},"links":{"cited_paper":"/paper/2012.13255","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:6aad628e3cac14b4a84f901e8fc2dc248cb569794fdf55ad15e5a59b0a5ebcfd","observation_id":"c037580d-f24e-4570-9790-0529512d6d44","resolution":{"observed_at":"2026-08-15T14:39:14.251062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.255038Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.255038Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:64b4e3c2ac74482662d85ea373072b0224892f9b0865c7e039856f31d78d8655","observation_id":"ce1f0373-7106-4b05-a835-fd2e8aded92d","resolution":{"observed_at":"2026-08-15T14:39:14.255038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.00051","last_updated":"2021-05-09T19:49:46Z","snapshot_observed_at":"2026-08-13T17:59:32.644760Z","submitted_at":"2020-07-31T19:37:59Z","title":"On the Convergence of SGD with Biased Gradients","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.00051","snapshot_observed_at":"2026-08-15T14:39:14.258448Z","title":"On the convergence of SGD with biased gradients.arXiv preprint arXiv:2008.00051, 2020.(Cited on page 350)","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.258448Z"},"links":{"cited_paper":"/paper/2008.00051","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:3af48ff61111da2d4891a8f2e7e34f2c0be26536044ec1520bd3da1c939d132b","observation_id":"083da089-c0d7-4c23-aade-eb09445b5529","resolution":{"observed_at":"2026-08-15T14:39:14.258448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.262286Z","title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.262286Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:70a2c6b06d65cbdc601413af9feb98be3c53dec4f104796ab926593eab082c39","observation_id":"373f3b4e-67df-40f0-b582-71335b705a37","resolution":{"observed_at":"2026-08-15T14:39:14.262286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.265782Z","title":"Byzantine stochastic gradi- ent descent","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.265782Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:200c16e8a8d6766e77e870afb795b07f0375220ce6ba49651222e2c86ffb8335","observation_id":"aecb71cf-1f76-4a56-8dd4-ed5a8d4b214a","resolution":{"observed_at":"2026-08-15T14:39:14.265782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.268733Z","title":"The convergence of sparsified gradient methods","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.268733Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:f8cc0b39af6c83b2841e1eea3edcdac13e16c542f9621e4ddc767a55d5ec4fb2","observation_id":"2d5ba978-feaa-4f67-909f-804e58013d64","resolution":{"observed_at":"2026-08-15T14:39:14.268733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.271324Z","title":"Byzantine-resilient non-convex stochastic gradient descent","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.271324Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:25c00c87b0e942d4b6d05a9610641d27958ca082edcb907acb11415945699588","observation_id":"301963da-b610-40e3-b1f0-bb884db3d823","resolution":{"observed_at":"2026-08-15T14:39:14.271324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.273868Z","title":"Fixing by mixing: A recipe for optimal byzantine ml under heterogeneity","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.273868Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:e2e997bf82b80b52e2bfe253dcf87735921c8b43021a08ae8ef30204e6b4fc80","observation_id":"59c7da2c-b3c0-4aee-a554-e677efd1fa34","resolution":{"observed_at":"2026-08-15T14:39:14.273868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12780","last_updated":"2024-06-10T13:43:21Z","snapshot_observed_at":"2026-08-15T17:20:11.755620Z","submitted_at":"2024-02-20T07:40:11Z","title":"Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12780","snapshot_observed_at":"2026-08-15T14:39:14.276427Z","title":"Tackling byzantine clients in federated learning.arXiv preprint arXiv:2402.12780, 2024a.(Cited on page 119)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.276427Z"},"links":{"cited_paper":"/paper/2402.12780","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:94d492220013c277047b32e44c7126be990bd662a376c6da56b5e13279619fab","observation_id":"f31d589e-462b-494e-935f-b28a49059d6f","resolution":{"observed_at":"2026-08-15T14:39:14.276427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.279479Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.279479Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:2357c2e499cd372c9d8514f363ddf3c758218975f2a714183cf801220618c37b","observation_id":"29787a14-b3c9-4a28-8e99-d22cd4814ae6","resolution":{"observed_at":"2026-08-15T14:39:14.279479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.282022Z","title":"Federated learning for healthcare: Systematic review and architecture proposal.ACM Transactions on Intel- ligent Systems and Technology (TIST), 13(4):1–23, 2022.(Cited on page 104)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.282022Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:073cd75071e03e02c9a4f9e0f474da5602cd82cfda042e6ac0e911ff413953e5","observation_id":"8b68e4f2-6608-49ce-9237-5136c45f2cda","resolution":{"observed_at":"2026-08-15T14:39:14.282022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.284694Z","title":"Communication complexity of distributed convex learning and optimization","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.284694Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:58e59b8114f36bb950e869ed32f8e16566768aab94c9c029fc4f3ba46f9cd58d","observation_id":"f6142c0f-3921-4caa-ac43-5caaf7bd4c2e","resolution":{"observed_at":"2026-08-15T14:39:14.284694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.288012Z","title":"Lower bounds for non-convex stochastic optimiza- tion.Mathematical Programming, 199(1-2):165–214, 2023.(Cited on pages 123 and 125)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.288012Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:70faca0c03ce185847666e47eb6106e74c4a5383dc70b930117cd4b5c067cf52","observation_id":"ba4ce198-d626-45f5-9f00-7ba3180ebecb","resolution":{"observed_at":"2026-08-15T14:39:14.288012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.08894","last_updated":"2021-05-27T06:41:45Z","snapshot_observed_at":"2026-08-03T13:43:52.031121Z","submitted_at":"2021-03-16T07:32:58Z","title":"Distributed Deep Learning Using Volunteer Computing-Like Paradigm","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.08894","snapshot_observed_at":"2026-08-15T14:39:14.291333Z","title":"Distributed deep learning using volunteer computing-like paradigm.arXiv preprint arXiv:2103.08894, 2021.(Cited on page 117)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.291333Z"},"links":{"cited_paper":"/paper/2103.08894","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:76e12a25db7da19daf28c194ddc6ba3501a73111e08d2a4cf31e4ec3c8403f40","observation_id":"1f5de97c-06ab-4259-b31e-0784aa000f6d","resolution":{"observed_at":"2026-08-15T14:39:14.291333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.294822Z","title":"A little is enough: Cir- cumventing defenses for distributed learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.294822Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:142efb52935d97f2ea49be6d764d14f418c501bc8c736a090982316e0512b62c","observation_id":"5a5dfccd-b09a-4827-a168-34c457a765a7","resolution":{"observed_at":"2026-08-15T14:39:14.294822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.297750Z","title":"Qsparse- local-SGD: Distributed SGD with quantization, sparsification and local computations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.297750Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:b1388e962f440857a5a8cd24316c20fc0eb588de10d58933cdf3c8f6e3982c1d","observation_id":"9e06ae32-c56e-46f3-bb57-4729b0bcdf14","resolution":{"observed_at":"2026-08-15T14:39:14.297750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.300706Z","title":"Generalized mono- tone operators and their averaged resolvents.Mathematical Programming, 189(1):55–74, 2021.(Cited on page 49)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.300706Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:1e1067c98cbd2c932e12efa918db11cebc2d4a40f3b8c13575e4947b50fa0032","observation_id":"a6c75a6d-950e-40b4-a300-481cb8f2c167","resolution":{"observed_at":"2026-08-15T14:39:14.300706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.303826Z","title":"MOS-SIAM Series on Optimization, 2017.(Cited on pages 43 and 63)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.303826Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:615dccde636da431ca30147abc06c48b4d3b21d185db55d541fc85124f9ee77d","observation_id":"b852965a-fc8f-4d71-9147-18ad2490ff35","resolution":{"observed_at":"2026-08-15T14:39:14.303826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.306845Z","title":"Demystifying parallel and distributed deep learning: An in-depth concurrency analysis.ACM Computing Surveys (CSUR), 52(4):1–43, 2019.(Cited on page 23)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.306845Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:e6a814bcd8d811eafb664c491a6cc17b11d5670212addcd142a6c21b4c8e330c","observation_id":"b165bf5a-a6db-4fe1-9f22-1c7edd7a663d","resolution":{"observed_at":"2026-08-15T14:39:14.306845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.309975Z","title":"Practical recommendations for gradient-based training of deep architectures","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.309975Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:65e26931bd66270169e0bdc99f4aeae77a86569d7627ebba32c2df1e7ed3cf44","observation_id":"de813356-f3cb-4d2a-b44a-f10c77aeaacd","resolution":{"observed_at":"2026-08-15T14:39:14.309975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05291","last_updated":"2019-02-22T19:55:48Z","snapshot_observed_at":"2026-08-14T18:15:53.973316Z","submitted_at":"2018-10-11T23:50:32Z","title":"signSGD with Majority Vote is Communication Efficient And Fault Tolerant","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05291","snapshot_observed_at":"2026-08-15T14:39:14.313033Z","title":"signsgd with majority vote is communication efficient and byzantine fault tolerant.arXiv preprint arXiv:1810.05291, 2018.(Cited on page 350)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.313033Z"},"links":{"cited_paper":"/paper/1810.05291","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:8c85071add045bee3c401ba220a90014e8ed30c3ee7612ffb792b8fe4c9ce408","observation_id":"0230b716-1afc-4184-80bb-fa3e79711ee2","resolution":{"observed_at":"2026-08-15T14:39:14.313033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.317457Z","title":"Incremental proximal methods for large scale convex optimization.Mathematical Programming, 129(2):163–195, 2011.(Cited on page 423)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.317457Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:b16732a53c694ac65131c627050aff0eadffde10ac0eb89652e8b7ac3f22c4c5","observation_id":"a5b19f17-f587-4c0a-b44c-66b1decba71e","resolution":{"observed_at":"2026-08-15T14:39:14.317457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.320477Z","title":"On biased compression for distributed learning.Journal of Machine Learning Research, 24(276):1–50, 2023.(Cited on pages 114, 121, 269, and 350)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.320477Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:3e5bb4bea874b37e6c638e657fec4a673f76fc375c3b2eaa7a03bb675e2468e0","observation_id":"9f66fa8c-92c2-41e0-9495-383763095f74","resolution":{"observed_at":"2026-08-15T14:39:14.320477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09673","last_updated":"2024-09-20T21:21:56Z","snapshot_observed_at":"2026-08-13T00:06:55.915196Z","submitted_at":"2024-05-15T19:27:45Z","title":"LoRA Learns Less and Forgets Less","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09673","snapshot_observed_at":"2026-08-15T14:39:14.323602Z","title":"LoRA learns less and forgets less.arXiv preprint arXiv:2405.09673, 2024.(Cited on page 132)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.323602Z"},"links":{"cited_paper":"/paper/2405.09673","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:1afb1532e1626bdec76873b48fcb75aac8741784b61c5cddb00e5a719d503a15","observation_id":"3062fa6c-744f-4f90-bb04-56cdb9857e29","resolution":{"observed_at":"2026-08-15T14:39:14.323602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.327099Z","title":"Machine learning with adversaries: Byzantine tolerant gradient descent","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.327099Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:573363b48ccf06c4b415ced79d607b9fa79eb2f5da1c2aec5446717f9f782882","observation_id":"b4ad7a4b-43f2-4639-8271-e0aaaa85dd90","resolution":{"observed_at":"2026-08-15T14:39:14.327099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.330265Z","title":"Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.330265Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:f29ec38811ee22ec9e4ff20b0bcbf942a3008cb6819871c647a27bda96489857","observation_id":"c9585973-869d-45a4-ba77-0788aebf6169","resolution":{"observed_at":"2026-08-15T14:39:14.330265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.333254Z","title":"Towards federated learning at scale: System design.Proceedings of machine learning and systems, 1:374–388, 2019.(Cited on page 24)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.333254Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:2b41a61799ac899f1e37491efa086339bb561095a570c840a9c1ea58ee504499","observation_id":"957d9540-c62c-44bf-bdd1-a95b219de095","resolution":{"observed_at":"2026-08-15T14:39:14.333254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.336427Z","title":"Curiously fast convergence of some stochastic gradient descent algorithms","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.336427Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:62828846fef513edd1b36f42932a7df76e4d0042a8e15f88c20712df1d7d7d73","observation_id":"e30da65c-2b94-42e6-bb85-7a67ccc691ee","resolution":{"observed_at":"2026-08-15T14:39:14.336427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.339648Z","title":"Democratizing machine learning: Resilient distributed learning with heterogeneous participants","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.339648Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:96fb1987129e17b5598df96ecba2a12a7d7dc2e70f6c1375b2c43e08f0343d68","observation_id":"bd92d279-3d4b-4eb3-b21a-97ffd506b1c4","resolution":{"observed_at":"2026-08-15T14:39:14.339648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.343167Z","title":"Minibatch stochastic three points method for unconstrained smooth minimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.343167Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d35a38e9bedf2d87fc84a64164943afda2bb69759f50709c292b9f63b6a420f4","observation_id":"a60a09f8-7400-43b9-bc1d-66facd631b4b","resolution":{"observed_at":"2026-08-15T14:39:14.343167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.345640Z","title":"Flpytorch: optimization research simulator for federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.345640Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:484f7738254bafc28157679cede1100b6ae9a5a54a8e3903a48d47c8699de575","observation_id":"98845fd0-8120-4089-adf0-e318fa4a6c53","resolution":{"observed_at":"2026-08-15T14:39:14.345640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.348090Z","title":"Tighter lower bounds for shuffling SGD: Random permutations and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.348090Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:50e21a30e304a445b62073abe52ff041fecb7a31b2f1230b923a35f4caf4f890","observation_id":"0600f874-9cad-4c69-92ae-0d1881f37a5a","resolution":{"observed_at":"2026-08-15T14:39:14.348090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.350708Z","title":"A first-order primal-dual algorithm for convex problems with applications to imaging.Journal of Mathematical Imaging and Vision, 40(1):120–145, 2011.(Cited on page 78)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.350708Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:b7735163138f09a1158c9327c11fe09b8d5f4646577e012abbfe366344b19fda","observation_id":"1e351160-cffb-4db7-9861-b02bd68a115d","resolution":{"observed_at":"2026-08-15T14:39:14.350708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.353739Z","title":"LIBSVM: A library for support 151 vector machines.ACM transactions on intelligent systems and technology (TIST), 2(3):1–27, 2011.(Cited on pages 57, 71, 87, 102, 129, and 269)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.353739Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:c3bc8851527c779439ea8f777868dc50d732515663da55a6a4a721a04c85fc93","observation_id":"af79b4c7-4815-4ca3-b09c-ceedc52d1cb4","resolution":{"observed_at":"2026-08-15T14:39:14.353739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00878","last_updated":"2020-07-02T04:45:55Z","snapshot_observed_at":"2026-08-10T13:40:17.085827Z","submitted_at":"2020-07-02T04:45:55Z","title":"On the Outsized Importance of Learning Rates in Local Update Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.00878","snapshot_observed_at":"2026-08-15T14:39:14.356201Z","title":"On the outsized importance of learn- ing rates in local update methods.arXiv preprint arXiv:2007.00878, 2020","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.356201Z"},"links":{"cited_paper":"/paper/2007.00878","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:331988ebabb6125588208d0a3094066fb05329aef5cfc7a139dc5a7a7638af46","observation_id":"63122e3e-f66f-4d91-b03e-1042ccb195b2","resolution":{"observed_at":"2026-08-15T14:39:14.356201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.07820","last_updated":"2021-06-15T00:40:40Z","snapshot_observed_at":"2026-08-15T16:18:23.194346Z","submitted_at":"2021-06-15T00:40:40Z","title":"On Large-Cohort Training for Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.07820","snapshot_observed_at":"2026-08-15T14:39:14.358958Z","title":"On large-cohort training for federated learning.arXiv preprint arXiv:2106.07820, 2021.(Cited on pages 60 and 90)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.358958Z"},"links":{"cited_paper":"/paper/2106.07820","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:32d4d1f95a51b65cca791509bc709c36802def8577e49843f00fb8cee4d3739d","observation_id":"072b9a17-ffaf-4c27-a479-6a3ba9ea0f6a","resolution":{"observed_at":"2026-08-15T14:39:14.358958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.362756Z","title":"Draco: Byzantine-resilient distributed training via redundant gradi- ents","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.362756Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d7309bff4c580d28d7b7426dbfe8cba1e6f6198584849166e5ad104780146b19","observation_id":"8e6bc20e-b4c4-44db-842a-0bde68b49562","resolution":{"observed_at":"2026-08-15T14:39:14.362756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.365724Z","title":"A primal–dual fixed point algorithm for convex separable minimization with applications to im- age restoration.Inverse Problems, 29(2):025011, 2013.(Cited on page 186)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.365724Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:a77c1f44aedd302997424e1db1af3876f99d00889d246276615d4af5fbd04965","observation_id":"30a75e98-f211-456d-9b9e-64a8eb716522","resolution":{"observed_at":"2026-08-15T14:39:14.365724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.368718Z","title":"Optimal client sampling for federated learning.Privacy Preserving Machine Learning (NeurIPS 2020 Workshop), 2020a.(Cited on pages 27, 30, 60, 75, and 90)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.368718Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:746dc761063541af6986d9cd3031d4df3353cfde7586626017e58563d7b66a25","observation_id":"37c625f0-09de-431d-9476-77e7106427f5","resolution":{"observed_at":"2026-08-15T14:39:14.368718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.371598Z","title":"Understanding gradient clipping in private SGD: A geometric perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.371598Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:339789d6df2c747f000de8a7d28c7a917e8a25c9eeb1785c1e0b39154d86698d","observation_id":"f9977c3c-9fb0-441d-97d2-3479dc7fcbf1","resolution":{"observed_at":"2026-08-15T14:39:14.371598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.374737Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.374737Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d7f98ab5006609a49b77c0d05d78b2640ae5473c1b633eeab706a767a0dd69a1","observation_id":"2681aab6-511b-439d-a6e5-2354dd99b915","resolution":{"observed_at":"2026-08-15T14:39:14.374737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.01243","last_updated":"2020-10-03T01:04:17Z","snapshot_observed_at":"2026-08-09T04:56:08.266143Z","submitted_at":"2020-10-03T01:04:17Z","title":"Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.01243","snapshot_observed_at":"2026-08-15T14:39:14.378853Z","title":"Client selection in feder- ated learning: Convergence analysis and power-of-choice selection strate- gies.arXiv preprint arXiv:2010.01243, 2020.(Cited on pages 30 and 60)","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.378853Z"},"links":{"cited_paper":"/paper/2010.01243","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:13e3007a00aac031929e057636c31972b2479f0036fa9bb976fcd8f88fcd278f","observation_id":"0ecbaf52-b436-45a3-88a4-b2f6926b5d19","resolution":{"observed_at":"2026-08-15T14:39:14.378853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.382252Z","title":"On the convergence of federated averaging with cyclic client participation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.382252Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:9f548af6c2e73281cac7eb577a73082dbbc4788311b772c3f959e85c05e069b7","observation_id":"2895380d-d36c-43d6-b151-26b1873483a6","resolution":{"observed_at":"2026-08-15T14:39:14.382252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.385306Z","title":"Emerging trends: A gentle introduction to fine-tuning.Natural Language Engineering, 27(6): 763–778, 2021.(Cited on page 131)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.385306Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:7f4fdf86b7222a37d0bdf2ff9a03471d8dbb9df580685296f159352ef5c678a1","observation_id":"3ec6e413-538c-40d6-8e82-12141ebaaff4","resolution":{"observed_at":"2026-08-15T14:39:14.385306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.388427Z","title":"Proximal splitting methods in signal processing","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.388427Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:c1d83686a9db86c693e157ca8a51f074c8bb1a3761c79a53b2f72aa725a49569","observation_id":"ac78e289-b439-4779-aa56-19590cd47797","resolution":{"observed_at":"2026-08-15T14:39:14.388427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.391372Z","title":"Combettes, Laurent Condat, Jean-Christophe Pesquet, and B˘ ang C","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.391372Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:171c87f1b4764af14b2da51f158bb3f6db1b26a4d39fbafc70715b55666a4c29","observation_id":"a0fb944f-5fa1-44ae-9486-16bd55422fe8","resolution":{"observed_at":"2026-08-15T14:39:14.391372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03056","last_updated":"2023-03-06T13:39:15Z","snapshot_observed_at":"2026-08-13T19:09:18.648659Z","submitted_at":"2021-06-06T07:39:01Z","title":"MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization","version":3},"cited_work":{"arxiv_id":"2106.03056","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.03056","snapshot_observed_at":"2026-08-15T14:39:16.718895Z","title":"MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization","venue":"math.OC","work_id":"3ebdbece-2d84-4c47-ae1d-32f53a3b913e","year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.394303Z"},"links":{"cited_paper":"/paper/2106.03056","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:45e01771d18541bee5b2baae64b336b1b5b4d76dc475e721d4bd2385cf1a3fe9","observation_id":"6e99eb70-a8bf-4b0a-8c2d-1a7f4a1bcb57","resolution":{"observed_at":"2026-08-15T14:39:16.722949Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12891","last_updated":"2023-03-07T06:50:23Z","snapshot_observed_at":"2026-08-13T14:56:57.121509Z","submitted_at":"2022-07-26T13:37:21Z","title":"RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12891","snapshot_observed_at":"2026-08-15T14:39:14.397597Z","title":"RandProx: Primal-dual opti- mization algorithms with randomized proximal updates.arXiv preprint arXiv:2207.12891, 2022.(Cited on pages 77, 78, 80, and 437)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.397597Z"},"links":{"cited_paper":"/paper/2207.12891","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:8736b6e673c5a64e23decf5eb2c5fb1a5876bc57e5c8cbdffbd35b8bb8e92a2b","observation_id":"8e8c59c8-18b3-4542-8759-5828255fd41f","resolution":{"observed_at":"2026-08-15T14:39:14.397597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00137","last_updated":"2023-02-24T08:24:22Z","snapshot_observed_at":"2026-08-12T05:38:51.036207Z","submitted_at":"2019-11-30T06:06:08Z","title":"Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists","version":8},"cited_work":{"arxiv_id":"1912.00137","doi":null,"metadata_source":"pith","pith_arxiv_id":"1912.00137","snapshot_observed_at":"2026-08-15T14:39:16.696419Z","title":"Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists","venue":"math.OC","work_id":"5aa56992-8f57-4208-a26f-b02cdeec4ea1","year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.400811Z"},"links":{"cited_paper":"/paper/1912.00137","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:91634f598b35c7ea1c6742cb1ab554dcdc5a3b998f48e56954fcc00b46754e5d","observation_id":"20e41091-570e-4177-b2f7-384b14a15fea","resolution":{"observed_at":"2026-08-15T14:39:16.700545Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.403779Z","title":"Distributed proximal splitting algorithms with rates and acceleration.Frontiers in Sig- nal Processing, page 12, 2022.(Cited on page 186)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.403779Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:40880895f25e16d1348ad13305bca13532332f15cb074722344eddc8e7f47d65","observation_id":"ec2b8597-2075-47c3-a425-2b96969b4706","resolution":{"observed_at":"2026-08-15T14:39:14.403779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.09832","last_updated":"2026-06-07T11:57:20Z","snapshot_observed_at":"2026-08-13T12:41:42.452544Z","submitted_at":"2023-02-20T08:37:44Z","title":"TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.09832","snapshot_observed_at":"2026-08-15T14:39:14.406150Z","title":"TAMUNA: Doubly accelerated federated learning with local training, com- pression, and partial participation.arXiv preprint arXiv:2302.09832, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.406150Z"},"links":{"cited_paper":"/paper/2302.09832","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:2e2827aa8ebe0828c097c2d9f5345f108916f06db01c6e9487d9af0563614332","observation_id":"4d263ad1-c0ef-4183-aca2-2959d032467d","resolution":{"observed_at":"2026-08-15T14:39:14.406150Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.409008Z","title":"Importance sampling for minibatches","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.409008Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:829dc492819719071c98a7959a196b713ad810bd1bbc83926c070f70848ad2d6","observation_id":"3bf18e55-5419-46ef-8ce3-0c236088da1d","resolution":{"observed_at":"2026-08-15T14:39:14.409008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.412140Z","title":"Momentum-based variance re- duction in non-convex SGD","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.412140Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:735c498874e4e39dc385545ac4ccd79fb49ad69c5279f8306af70627625973b8","observation_id":"7099af46-8835-4d4c-9019-3304609c4b70","resolution":{"observed_at":"2026-08-15T14:39:14.412140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.415902Z","title":"Asynchronous byzantine machine learning (the case of sgd)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.415902Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:cac38d3e717c72ad5decfaebdcdfce5c5d7dd7b5f1fcccafdd5ce31380a2a026","observation_id":"cd868ca3-fa3f-4930-9a79-88d61e34c5f8","resolution":{"observed_at":"2026-08-15T14:39:14.415902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.419207Z","title":"Aggregathor: Byzantine machine learn- ing via robust gradient aggregation.Proceedings of Machine Learning and Systems, 1:81–106, 2019.(Cited on page 118)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.419207Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:308236022d81021c68e0fc899b1013213d1318ae03c3dded17af87b54a4f16b9","observation_id":"57402cd9-959f-4e8b-892d-4bff3054207f","resolution":{"observed_at":"2026-08-15T14:39:14.419207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.423242Z","title":"Re- cent theoretical advances in non-convex optimization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.423242Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:ac8c766f75a6efe91bb37d46e3a67ec0f5ed88dcc91a04ed89d6679ce8794170","observation_id":"329717b0-9cbb-433d-a0f1-4589a9a892d5","resolution":{"observed_at":"2026-08-15T14:39:14.423242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.426304Z","title":"Byzantine-resilient high-dimensional SGD with local iterations on heterogeneous data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.426304Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:06cc64802f20b7e7839d141f5750a794501030b86d19579523cc20044b0be9fd","observation_id":"803df5b7-9039-456f-853f-4d31c653f279","resolution":{"observed_at":"2026-08-15T14:39:14.426304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.429487Z","title":"A three-operator splitting scheme and its optimization applications.Set-Val","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.429487Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:00afa410923310ccb8652a24afcbda947c971dd87328987b6c42d2845fa9e435","observation_id":"102ae155-77d6-43fc-a40f-522a77bf53a4","resolution":{"observed_at":"2026-08-15T14:39:14.429487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.432520Z","title":"Large scale distributed deep networks.Advances in neural information processing systems, 25, 2012.(Cited on page 23)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.432520Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:38d34a16442c27229de7834bf105b4ce035d0c10520489fc08377db73ba7916a","observation_id":"b93ec3c9-dbcf-4e32-8523-790d594ac15f","resolution":{"observed_at":"2026-08-15T14:39:14.432520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.435614Z","title":"A simple practical accelerated method for finite sums.29th Conference on Neural Information Processing Systems (NeurIPS), 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.435614Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:26c395ea582d49d525ebdb59be2b9e111757f70e9ada2888756c8e876f1000ac","observation_id":"413550d4-17cc-483f-8013-2dbb524a043f","resolution":{"observed_at":"2026-08-15T14:39:14.435614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.438473Z","title":"On the ineffectiveness of variance reduced optimization for deep learning.Advances in Neural Information Processing Systems, 32, 2019.(Cited on page 129)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.438473Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:5458e93c38b579495deecae95ff197222a0dbdd4ad2a2b523a43e48fd0f0e3ec","observation_id":"2917509a-abef-4645-a5df-2bce3add0361","resolution":{"observed_at":"2026-08-15T14:39:14.438473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.441391Z","title":"SAGA: A fast incremental gradient method with support for non-strongly convex com- posite objectives","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.441391Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:b2642f6e528812ae69cdb6af3cad4f56c5e0e004c47821ba4e02a86d387c86ab","observation_id":"f3d76e7f-4cca-4c8d-91a4-b90f38acbfce","resolution":{"observed_at":"2026-08-15T14:39:14.441391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.444400Z","title":"Optimal dis- tributed online prediction using mini-batches.Journal of Machine Learning Research, 13(1):165–202, January 2012.(Cited on pages 30 and 53)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.444400Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:6375874ba5eb5a41526572c2969979c644f6936086e782c69e721000fc8d41f3","observation_id":"d10e0071-7c9c-4dfd-8171-356b2a73873c","resolution":{"observed_at":"2026-08-15T14:39:14.444400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.447341Z","title":"Mast: Model-agnostic sparsified training.arXiv preprint arXiv:2311.16086, 2023a.(Cited on page 39)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.447341Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:694c9e86b84b6ecc563c302e9f8f1a06970a996333485d37445ad01f9e17e793","observation_id":"8c362c72-a511-4beb-9e96-b96c0a3d9663","resolution":{"observed_at":"2026-08-15T14:39:14.447341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.451515Z","title":"A guide through the Zoo of biased SGD","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.451515Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:f1990a61818ff8b57791453e936620bac8eaedd06c4bb94a7f20b8053085f427","observation_id":"701a9742-b520-49ef-bed8-3fb7dbafbe82","resolution":{"observed_at":"2026-08-15T14:39:14.451515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06677","last_updated":"2024-03-11T12:49:37Z","snapshot_observed_at":"2026-08-13T00:57:41.419989Z","submitted_at":"2024-03-11T12:49:37Z","title":"Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction","version":1},"cited_work":{"arxiv_id":"2403.06677","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.06677","snapshot_observed_at":"2026-08-15T14:39:16.597761Z","title":"Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction","venue":"cs.LG","work_id":"fe264af9-86fb-43bb-bd5a-8a1a52606d66","year":2024},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.454654Z"},"links":{"cited_paper":"/paper/2403.06677","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:2f98191ddd4d5c952323b63f09c79a16ca624a424f4a803bde00575e1695e7af","observation_id":"a8dc9673-dca5-4fa6-9fa7-52710fde2d31","resolution":{"observed_at":"2026-08-15T14:39:16.601338Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02781","last_updated":"2025-04-11T10:52:32Z","snapshot_observed_at":"2026-08-11T23:04:44.996769Z","submitted_at":"2024-12-03T19:20:56Z","title":"Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization","version":3},"cited_work":{"arxiv_id":"2412.02781","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.02781","snapshot_observed_at":"2026-08-15T14:39:16.584831Z","title":"Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization","venue":"math.OC","work_id":"02b3176a-3fc0-4268-b58d-6a7a49f03904","year":2024},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.458028Z"},"links":{"cited_paper":"/paper/2412.02781","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:5113ab3ecde6fc408c38f61e2471a52b94eb7031ab5746aa507110fefad439fa","observation_id":"3a9c108e-17e0-4d89-ae90-aadf73cd8384","resolution":{"observed_at":"2026-08-15T14:39:16.588570Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.461988Z","title":"Distributed deep learning in open collaborations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.461988Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d5c71b9f253dc214b39a15a19940e010676f8763bbc132974f5737d35b8a878c","observation_id":"e7692ab1-8f9e-4c32-a8b3-95141eb679c7","resolution":{"observed_at":"2026-08-15T14:39:14.461988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.464980Z","title":"A simple algorithm for a class of nonsmooth convex–concave saddle-point problems.Operations Research Letters, 43(2):209–214, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.464980Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:4372f46a028163a5dd9cc8a0cd8e256d981ca17af06c8dafd85cd51748a12ec3","observation_id":"90513fe8-1277-41db-864d-8bd872bb7169","resolution":{"observed_at":"2026-08-15T14:39:14.464980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.467374Z","title":"The algorithmic foundations of differential privacy.Foundations and trends®in theoretical computer science, 9(3-4): 211–487, 2014.(Cited on pages 23, 26, and 28)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.467374Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:af59249505c98544fb77fe83c41e32aa4ee1c8a0269807782811c96c43d546a2","observation_id":"5e2942f1-3b96-4683-81fc-969d60ec853d","resolution":{"observed_at":"2026-08-15T14:39:14.467374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.469835Z","title":"Cali- brating noise to sensitivity in private data analysis","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.469835Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:5c6cd6109e6f14eedd9476e269f395b246346217c5b5433cc7de3b7b335a9515","observation_id":"462db7c5-f08d-4a75-8279-31b0a9fa18ee","resolution":{"observed_at":"2026-08-15T14:39:14.469835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.472283Z","title":"Eichner, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.472283Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:4a9c34328a09b50ffba2f87e2f2687b888eead8570f0470563e3298ba1070746","observation_id":"845998f0-7c53-43fd-8211-0740d797255f","resolution":{"observed_at":"2026-08-15T14:39:14.472283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.474648Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.474648Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:4efe00fb64aec97364fda15bbc4f23bad73920c03a6cf8a047e8c2bd544818b7","observation_id":"ede8a3ce-054f-4b48-8df5-e4070b01e7d0","resolution":{"observed_at":"2026-08-15T14:39:14.474648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.477164Z","title":"Spider: Near- optimal non-convex optimization via stochastic path-integrated differential estimator","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.477164Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:70b167598dd48ac69379bc4f20203402cf94d2a3d6ec0d1254fcf19369f26528","observation_id":"a5277e9b-2d54-4ca2-b023-afec68448b38","resolution":{"observed_at":"2026-08-15T14:39:14.477164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.479558Z","title":"AFLGuard: Byzantine-robust asynchronous federated learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.479558Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:a27ebe93cb3540a1605f37d0c3501830065c417453bea333ed3dffaa6b969c2e","observation_id":"dfe02a52-ed10-4548-a311-cb65346e1921","resolution":{"observed_at":"2026-08-15T14:39:14.479558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.03294","last_updated":"2025-06-20T17:11:24Z","snapshot_observed_at":"2026-08-13T22:09:41.395394Z","submitted_at":"2021-10-07T09:29:14Z","title":"EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.03294","snapshot_observed_at":"2026-08-15T14:39:14.481867Z","title":"Ef21 with bells & whistles: Practical algorithmic extensions of modern error feedback.arXiv preprint arXiv:2110.03294, 2021.(Cited on pages 273, 274, and 350)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.481867Z"},"links":{"cited_paper":"/paper/2110.03294","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:0283c3ae53de9b3447c2c377e3bd6f16fc17c9e2b95859a6ccd07b3f7c8008b6","observation_id":"38bcc988-12ed-4bf9-948a-ccde50506231","resolution":{"observed_at":"2026-08-15T14:39:14.481867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.485070Z","title":"Efficient evaluation of scaled proxi- mal operators.Electronic Transactions on Numerical Analysis, 46:1–23, 03 2016.(Cited on page 44)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.485070Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:36911d94e0102d7c6018c1c3c8d672bc26be3a1be601893a9982522142108444","observation_id":"8b44f6ca-300b-47e4-b505-cd88d09797dd","resolution":{"observed_at":"2026-08-15T14:39:14.485070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.488048Z","title":"Stochastic first-and zeroth-order meth- ods for nonconvex stochastic programming.SIAM journal on optimization, 23(4):2341–2368, 2013.(Cited on page 406)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.488048Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:4de192553c4089116bdbe4e9ab03857818ab32056bd806d68e64f1d52052d5b8","observation_id":"76beb639-582a-4c1c-acde-11e3152683ec","resolution":{"observed_at":"2026-08-15T14:39:14.488048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.491232Z","title":"Distributed new- ton can communicate less and resist Byzantine workers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.491232Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:81a20a8c6b38979752f1083c8ee6763ca754427c416a92179cf77fd1d1c0cdec","observation_id":"f48c4445-310c-47e6-b104-f1c98fbbb567","resolution":{"observed_at":"2026-08-15T14:39:14.491232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.494311Z","title":"Communication-efficient and byzantine-robust dis- tributed learning with error feedback.IEEE Journal on Selected Areas in Information Theory, 2(3):942–953, 2021.(Cited on page 350)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.494311Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:7ab6b937b7ac4d7298890a92f96e43ff350c59dec05aacfe01e0fc814f2b8de3","observation_id":"9850626d-ae01-4164-9414-dc731dab4b76","resolution":{"observed_at":"2026-08-15T14:39:14.494311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.497443Z","title":"Sharp bounds for federated averaging (local sgd) and continuous perspective","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.497443Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:41f2684863a7024793274dcd4b8523cfa3df1d83c698599d1951301718acfa21","observation_id":"98b6c306-b56b-4b6f-8632-905cca1ad46b","resolution":{"observed_at":"2026-08-15T14:39:14.497443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.500729Z","title":"Television by pulse code modulation.Bell System Technical Journal, 30(1):33–49, 1951.(Cited on page 121)","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.500729Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:0646a504aef53ea15310341fd6aec811f5f8c105c46f8d6fb78d1e0815c70cc2","observation_id":"e13a42c0-db6a-4167-afac-bcc88db021a9","resolution":{"observed_at":"2026-08-15T14:39:14.500729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.503899Z","title":"Stochas- tic optimization with heavy-tailed noise via accelerated gradient clipping","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.503899Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:0364517169ac7bab8b046ca4606958518d36b29c6cbe3b7455e91f6aae5a7336","observation_id":"d83c83e1-e647-42a4-8fad-44b4b43bfc5a","resolution":{"observed_at":"2026-08-15T14:39:14.503899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.506982Z","title":"A unified theory of SGD: Variance reduction, sampling, quantization and coordinate descent","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.506982Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:423e10e64f1b354d56c3eec50acdecd64acec7f0a80994eaddca588ea1275fc1","observation_id":"2a6ff18b-a2a0-4584-87ba-91c5066a0cd2","resolution":{"observed_at":"2026-08-15T14:39:14.506982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.510993Z","title":"Linearly converging error compensated SGD.Advances in Neural Information Processing Systems, 33:20889–20900, 2020c.(Cited on page 65)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.510993Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:1596350f5c7fb5d5f266e07069b23d788db6b42084649cddf70dcf9ac4d22fe8","observation_id":"552b9fc9-f733-4477-9dba-302ba0fdce4d","resolution":{"observed_at":"2026-08-15T14:39:14.510993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.513850Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.513850Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:c8cf1f31008613be27e6a1420b44075d6143c130cd8454ececaea273107f5269","observation_id":"45e6f806-b857-4c1c-b2c3-6c28e610468c","resolution":{"observed_at":"2026-08-15T14:39:14.513850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.516753Z","title":"Local SGD: Unified theory and new efficient methods","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.516753Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:819ef80192bd3a8076ce54db9f814726523edc8cc97ae017f4a9604873ed154e","observation_id":"1db21339-1087-43f7-ad6a-14e94aa48771","resolution":{"observed_at":"2026-08-15T14:39:14.516753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11257","last_updated":"2023-01-02T03:24:04Z","snapshot_observed_at":"2026-07-06T11:21:27.749737Z","submitted_at":"2021-06-21T17:00:42Z","title":"Secure Distributed Training at Scale","version":4},"cited_work":{"arxiv_id":"2106.11257","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.11257","snapshot_observed_at":"2026-08-15T14:39:16.563590Z","title":"Secure Distributed Training at Scale","venue":"cs.LG","work_id":"eb747219-af3e-4109-9c4a-e4c5629a27f3","year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.519705Z"},"links":{"cited_paper":"/paper/2106.11257","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:14a86abdfab07b1f44a79b49b0ddbdece5c5bbbfc2aa4a040817fd2cd847c148","observation_id":"72a5e46f-f765-41f0-9190-1c0d248f167d","resolution":{"observed_at":"2026-08-15T14:39:16.567536Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.522884Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.522884Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:af23e22e63c6a6f31f67725c2f9449c15b24059ea26bc61b3ea039ed00d02e1c","observation_id":"34e8dd34-6a15-4277-ad3b-3b8f1ee9fbc0","resolution":{"observed_at":"2026-08-15T14:39:14.522884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.526550Z","title":"Variance-reduced methods for machine learning.Proceedings of the IEEE, 108(11):1968–1983, 2020.(Cited on pages 30 and 349) 157","venue":null,"work_id":null,"year":1968},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.526550Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:3d9179dc864da188f96f4ef8915e321dd77d1d6c6c61840a8d1f4ccc89cd1db4","observation_id":"ba027ad6-bed5-4692-bd64-cdc06fbde200","resolution":{"observed_at":"2026-08-15T14:39:14.526550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.529674Z","title":"Gower, Peter Richt´ arik, and Francis Bach","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.529674Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:86f7b92330619e99a7fa7392fd8608ad24661a4de864acd7d4763871ea9254e3","observation_id":"2efb3cc9-195a-4544-b9ab-80350fb40e51","resolution":{"observed_at":"2026-08-15T14:39:14.529674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.532630Z","title":"SGD: General analysis and improved rates","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.532630Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:b57337b221f9a58f612244bf71456526bc0c4938833717bdcc6fa8330b989775","observation_id":"5ea93c2e-9c6c-47f8-a3b7-50a62de60dcd","resolution":{"observed_at":"2026-08-15T14:39:14.532630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02677","last_updated":"2018-04-30T21:53:41Z","snapshot_observed_at":"2026-08-09T05:23:26.365677Z","submitted_at":"2017-06-08T16:51:53Z","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02677","snapshot_observed_at":"2026-08-15T14:39:14.535736Z","title":"Accurate, large minibatch sgd: training imagenet in 1 hour.arXiv preprint arXiv:1706.02677, 2017.(Cited on page 104)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.535736Z"},"links":{"cited_paper":"/paper/1706.02677","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:0a825f4bc574eb123bee4d73b37df00ba824df15847337983ad6c300e559b30c","observation_id":"b83028b8-c34b-4cc2-aa5a-3b6205863c78","resolution":{"observed_at":"2026-08-15T14:39:14.535736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.539390Z","title":"Can 5th gen- eration local training methods support client sampling? yes! InInterna- tional Conference on Artificial Intelligence and Statistics, pages 1055–1092","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.539390Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:96e21b80333346b8f542994db119eb3caed506bb9e73324166c4e01a2d0f3f6d","observation_id":"2dd4ae5e-ef6d-4159-9789-60faa3b44952","resolution":{"observed_at":"2026-08-15T14:39:14.539390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03240","last_updated":"2023-06-05T20:50:36Z","snapshot_observed_at":"2026-08-13T11:23:55.677422Z","submitted_at":"2023-06-05T20:50:36Z","title":"Improving Accelerated Federated Learning with Compression and Importance Sampling","version":1},"cited_work":{"arxiv_id":"2306.03240","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.03240","snapshot_observed_at":"2026-08-15T14:39:16.542699Z","title":"Improving Accelerated Federated Learning with Compression and Importance Sampling","venue":"cs.LG","work_id":"3c3464ab-19a6-41d9-ae2e-c3866e16d5de","year":2023},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.542277Z"},"links":{"cited_paper":"/paper/2306.03240","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:93872fca50517661dc9095fde85c16dbfe6c1ce083a1d5afb5c3b3fc3b801da6","observation_id":"7a3bcec7-2b3e-4a9e-974a-551b5cd79bf5","resolution":{"observed_at":"2026-08-15T14:39:16.546131Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.14425","last_updated":"2019-12-06T21:16:41Z","snapshot_observed_at":"2026-07-30T21:49:53.494348Z","submitted_at":"2019-10-31T12:52:55Z","title":"On the Convergence of Local Descent Methods in Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.14425","snapshot_observed_at":"2026-08-15T14:39:14.545132Z","title":"On the convergence of local descent methods infederated learning.arXiv preprint arXiv:1910.14425, 2019.(Cited on pages 61 and 76)","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.545132Z"},"links":{"cited_paper":"/paper/1910.14425","citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:3c046a35b06dcbe77f2cfa65f04b1f43bd8ac2857f06e340f91cf8439075130f","observation_id":"e9b6cf79-beae-411a-8ff3-45d286249730","resolution":{"observed_at":"2026-08-15T14:39:14.545132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:39:14.547983Z","title":"Federated learning with compression: Unified analysis and sharp guarantees","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-15T14:39:14.547983Z"},"links":{"citing_paper":"/paper/2608.06563"},"observation_digest":"sha256:d39778a89a3c0af6d6b27d35ad7876954c42ac2d733c61eec511858c370ec4b9","observation_id":"7a83a56e-40df-481a-9afb-2a9787a72a16","resolution":{"observed_at":"2026-08-15T14:39:14.547983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.06563","last_updated":"2026-08-06T20:20:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T14:29:18.109897Z","submitted_at":"2026-08-06T20:20:52Z","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":94,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":300},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2608.06563."}