{"as_of":"2026-08-09T02:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a0a1af4435999fc9884c131fae826835e04d63d1916dd04515136ae52130fecc","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:04:28.363345Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T15:16:32.125215Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T15:17:39.400400Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2506.06337","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.06337","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Murhekar, A., Yuan, Z., Ray Chaudhury, B., Li, B., and Mehta, R","venue":null,"work_id":"bf9ceb21-9392-4478-9bb9-b95d8ccfce07","year":null},"citing_paper":{"arxiv_id":"2605.15520","last_updated":"2026-05-15T01:34:55Z","snapshot_observed_at":"2026-08-01T19:09:56.685096Z","submitted_at":"2026-05-15T01:34:55Z","title":"On the Fragility of Data Attribution When Learning Is Distributed","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-19T15:16:32.125215Z"},"links":{"cited_paper":"/paper/2506.06337","citing_paper":"/paper/2605.15520"},"observation_digest":"sha256:52fb6a3421aa298e3c3c746567ea662ce2e3ac07d9d2ef5b8ed2a15658dd2746","observation_id":"78d3d06f-15b7-413a-a069-a86458805a59","resolution":{"observed_at":"2026-05-19T15:17:39.403774Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.06337/citation-record","integrity":"/paper/2506.06337/integrity","json":"/paper/2506.06337/citation-record.json","paper":"/paper/2506.06337"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:35.452361Z","title":"On the privacy-robustness-utility trilemma in distributed learning,","venue":null,"work_id":"7c2eeabe-1976-4352-b2fc-ebcf39b7cf61","year":2023},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.345700Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:f3f766aefc791a3ab8ef45fc6236959de1850ed1cac6a819b4998b5d728fd99c","observation_id":"7819503f-6c96-458d-9dc1-4e6980936795","resolution":{"observed_at":"2026-08-07T12:04:35.499934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14390","last_updated":"2022-03-05T20:30:32Z","snapshot_observed_at":"2026-07-06T09:42:35.058716Z","submitted_at":"2020-07-28T17:59:07Z","title":"Flower: A Friendly Federated Learning Research Framework","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.14390","snapshot_observed_at":"2026-08-07T12:04:23.403233Z","title":"Flower: A friendly federated learning research framework,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.403233Z"},"links":{"cited_paper":"/paper/2007.14390","citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:8f8b626542a3f2c71377e031b4600d3db259d5fec43b983c650843bb6b341af9","observation_id":"2bbe8589-bc4a-443d-b422-6fef36f759c3","resolution":{"observed_at":"2026-08-07T12:04:23.403233Z","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":"2026-08-07T12:04:23.509121Z","title":"API design for machine learning software: experiences from the scikit-learn project,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.509121Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:a5cee28b12a74bef7965646325c59244edc58ff9b3b418650779402c13a461ee","observation_id":"abb45d22-9b0d-4852-a0de-91df25b10e13","resolution":{"observed_at":"2026-08-07T12:04:23.509121Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:35.229709Z","title":"Bayesian coreset optimization for personalized federated learning,","venue":null,"work_id":"2da02717-10d2-4991-871b-bceabbbd1a42","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.623784Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:0a2a4fde853e1e544db2b55faf6c1612dbce46fc876d3011c2d6be006f8c1c99","observation_id":"6178aa14-96d9-4eac-9291-4465b9756508","resolution":{"observed_at":"2026-08-07T12:04:35.296062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:35.116248Z","title":"Calibre: Towards fair and accurate personalized federated learning with self-supervised learning,","venue":null,"work_id":"5bd0924c-f5e6-4ea3-ae0b-30a7334296bc","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.724011Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:b0ac9b6320171b5778bf827e7d87c7242b934fde7d8e13657689fab655a9e02c","observation_id":"eb22865c-e1a5-4f9c-9d8c-94bda6b3f57a","resolution":{"observed_at":"2026-08-07T12:04:35.158096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:34.896220Z","title":"Momentum benefits non-iid federated learning simply and provably,","venue":null,"work_id":"44fe8876-431c-4cf3-a95b-e3a3cb94134c","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.803527Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:7c502bffba3d2c1951206598e1d628dc735169de7425c619ae38f554fdc927f0","observation_id":"7a8e67b2-59d5-45b8-8073-7dddc61db307","resolution":{"observed_at":"2026-08-07T12:04:34.982920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:34.693751Z","title":"Muc-5 evaluation metrics,","venue":null,"work_id":"133104ec-7102-4186-9682-10c4282102ab","year":1993},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.881274Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:c0f87ae672ec7d187d22e30099e05d8fd1e255d4835ae6dcce18610af635f67b","observation_id":"dc9e32b0-bb5c-4fbc-b801-6b6e8ea1aa22","resolution":{"observed_at":"2026-08-07T12:04:34.801798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:34.460441Z","title":"Feddc: Federated learning with non-iid data via local drift decoupling and correction,","venue":null,"work_id":"c32281ea-e515-4ec6-a3ac-a405ac5d6b2a","year":2022},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:23.959422Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:7afec624ca7066b376bf60504c7dd0f275fc8c1203f88717aa597918bf4d8119","observation_id":"e9bcdc12-2250-4c2c-9afb-82d682685529","resolution":{"observed_at":"2026-08-07T12:04:34.573365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:34.251759Z","title":"Deep reinforcement learning for modelling protein complexes,","venue":null,"work_id":"d6d18fd8-ef98-49c4-94ba-a0e09fd3b94c","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.016318Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:188d1a0fbeceed6003362d050832273971c795cf0291db9b99548886b6a10daf","observation_id":"fb9c18c0-8a8f-441e-ba1e-0455d4456473","resolution":{"observed_at":"2026-08-07T12:04:34.330322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:34.072595Z","title":"Goodfellow, Y","venue":null,"work_id":"6a71b4de-9a94-45be-a473-7b2809d9da61","year":2016},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.176731Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:7b52bc5f45494ad549dcb8fc8ad1a3fe1a3f85d5142d84eed9be09e0fc59c4e4","observation_id":"7a732ec5-9ab4-4cd7-94dd-e7e54d7647b1","resolution":{"observed_at":"2026-08-07T12:04:34.138536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:24.201265Z","title":"Array programming with NumPy,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.201265Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:ab4f31c43c49cc40853fcd00f219582bc5c7f76524f621a13b0db7a7b9c69bcf","observation_id":"8a92c733-e5f6-49cc-b833-b0136c25ba55","resolution":{"observed_at":"2026-08-07T12:04:24.201265Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:33.856783Z","title":"pfedbea: Combatting data heterogeneity for personalized federated learning by body exchange and aggregation abandon,","venue":null,"work_id":"edb5a567-9805-4d4d-a0e8-a0df11bbb568","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.216507Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:7b2281b8b4080a4a3f4aabaf2a40a4eabafeb2c2dedd46687c44e6a6441770d5","observation_id":"b24d9cc0-daab-4dbd-9399-23e752b1505a","resolution":{"observed_at":"2026-08-07T12:04:33.902881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:24.339553Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.339553Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:efc14f5373e892953cf416cfd4d1755c991714442ccf41f711892031e3b6fa9e","observation_id":"b7734355-699d-4633-838c-dc4c1a35647f","resolution":{"observed_at":"2026-08-07T12:04:24.339553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.06335","last_updated":"2019-09-13T17:26:20Z","snapshot_observed_at":"2026-08-02T11:40:53.964079Z","submitted_at":"2019-09-13T17:26:20Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06335","snapshot_observed_at":"2026-08-07T12:04:24.477660Z","title":"Measuring the effects of non- identical data distribution for federated visual classification,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.477660Z"},"links":{"cited_paper":"/paper/1909.06335","citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:aa2a75a44e3a33de92898957baecf52fcb68d7bf19277e2865b3c7fa7420b962","observation_id":"68d221fc-b35e-47f4-81ab-5bce81c12328","resolution":{"observed_at":"2026-08-07T12:04:24.477660Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:33.626437Z","title":"Stochastic controlled averaging for federated learning with communication compression,","venue":null,"work_id":"5b81edef-d60c-4031-9392-82af8a68031a","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.587677Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:4775933e197d7393d61c095509018dcf3d37b69aeecf1e4ee530761c82832c35","observation_id":"024ec56b-3932-47c3-bd5b-1f74dac0a949","resolution":{"observed_at":"2026-08-07T12:04:33.708788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:24.728452Z","title":"Per- sonalized cross-silo federated learning on non-iid data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.728452Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:ef7e4251d3043eeeb53b45dc7cf5b45b93863311429793578419ef7b72cd9a70","observation_id":"ac57a09e-75e8-41c4-88c0-158d4385109d","resolution":{"observed_at":"2026-08-07T12:04:24.728452Z","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":"2026-08-07T12:04:24.909254Z","title":"Matplotlib: A 2d graphics environment,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:24.909254Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:5cc51aa68d8a71499772dfa8623334d719af9ae1a7371a1259a15eab849b5140","observation_id":"5854e26c-786b-425a-861b-3db188db03e4","resolution":{"observed_at":"2026-08-07T12:04:24.909254Z","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":"2026-08-07T12:04:25.064274Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.064274Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:3c417c75d74df65cd39cc56bc618a38ef23f5361ea2bd0a21508bec5c5363ded","observation_id":"2a7be037-fffc-4c73-b6fd-42695acecdfe","resolution":{"observed_at":"2026-08-07T12:04:25.064274Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:33.239665Z","title":"Heterogeneous personalized federated learning by local-global updates mixing via convergence rate,","venue":null,"work_id":"2aaf64ea-4dbc-4e7d-ab33-8c7e1c396fc5","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.216291Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:94490ab1d2749e191c16857c4a9273a0d9207ee4a9c06988f3ba7f6a3b738a9c","observation_id":"e070804f-a755-4225-af81-f8fbcf01ffc9","resolution":{"observed_at":"2026-08-07T12:04:33.343632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:33.127464Z","title":"DepthFL : Depthwise federated learning for heterogeneous clients,","venue":null,"work_id":"c0bfdada-618a-4d68-a0d0-9b0fcbf52298","year":2023},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.384329Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:6032bc61ff3498d26d84217579b0c70d70466ffa117e0951d46337fdd306427c","observation_id":"7bf0d50d-0838-4371-9340-c178dba4e8d2","resolution":{"observed_at":"2026-08-07T12:04:33.162002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:25.491717Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.491717Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:37d6f8611c37fbcba78bcd1e758937ae35e582118d29b4329dc456c3a7c8cbcd","observation_id":"89b1f2e8-5157-4995-970c-f35dd4aa7c91","resolution":{"observed_at":"2026-08-07T12:04:25.491717Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:32.855288Z","title":"Lapan, Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more","venue":null,"work_id":"e053f5fe-ebf2-46e9-bf9b-d3f31e4f3369","year":2020},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.582480Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:999cba5b2d44d9dbb5fe3c7bd0f70ed32254420727eff457b3b13883d3967001","observation_id":"8bf4c27b-4770-4fd5-b34f-39045dc33f99","resolution":{"observed_at":"2026-08-07T12:04:32.967514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:25.684385Z","title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.684385Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:502c7a39975ed68e442eb323851300a568c407fc3d6b30fb211d9afb7e61551c","observation_id":"e86e0006-6ae2-4660-856e-fa381c0b2344","resolution":{"observed_at":"2026-08-07T12:04:25.684385Z","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":"2026-08-07T12:04:25.791603Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.791603Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:a186d033e64078dbdf6ce9c817e8a89d9c6e6a43f8e6427f325a237ba61336ff","observation_id":"278f9042-0adc-4ddf-ac08-2579afdf0fab","resolution":{"observed_at":"2026-08-07T12:04:25.791603Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:32.494873Z","title":"Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,","venue":null,"work_id":"b1647b10-a132-4cec-a99b-cdf5b4a16fe5","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:25.893925Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:6b8e62274dc9eb457bc06a972c0975313c9b8ff84b55da9ff5efbf533764f98e","observation_id":"b017d2b5-ea37-48ac-ba00-dce93e656502","resolution":{"observed_at":"2026-08-07T12:04:32.663621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:32.275743Z","title":"Federated learning with data- free distillation for heterogeneity-aware autonomous driving,","venue":null,"work_id":"f4c5cb58-5de8-4d03-af45-2e23aa1afd6f","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.051068Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:33297898e53d36f5fc188564cac8d54f52bbdef6c72405e7898e443c910af169","observation_id":"064448ee-9e22-4e38-855d-c01523de9db5","resolution":{"observed_at":"2026-08-07T12:04:32.360352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:32.088363Z","title":"Continuous control with deep reinforcement learning,","venue":null,"work_id":"abe965e2-4ae3-4aa3-961d-e150c3c676a0","year":2016},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.250937Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:701b4b57da9cc91e8ce654380f1c9d719afda53b819321bf55298e28fce5e1e2","observation_id":"b37e05bf-369b-41c5-be0d-acadaec43875","resolution":{"observed_at":"2026-08-07T12:04:32.165141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15005","last_updated":"2025-07-04T03:49:02Z","snapshot_observed_at":"2026-08-06T12:12:56.538005Z","submitted_at":"2025-01-25T00:47:37Z","title":"DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning","version":2},"cited_work":{"arxiv_id":"2501.15005","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.15005","snapshot_observed_at":"2026-08-07T12:04:28.549409Z","title":"DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning","venue":"cs.LG","work_id":"de92563b-30b7-4fbd-b4b8-bf62859e91dd","year":2025},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.365677Z"},"links":{"cited_paper":"/paper/2501.15005","citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:c404faab64faa71c28f0e6ccf93f57180cf864f81e7b78145fa9e12ac1acbfb8","observation_id":"0fbb1015-cff3-4567-9bb4-71919109b242","resolution":{"observed_at":"2026-08-07T12:04:28.640314Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:31.887240Z","title":"Integrating planning and deep reinforcement learning via automatic induction of task substructures,","venue":null,"work_id":"8d04b205-ed48-482a-a1a5-da5a17be4065","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.480271Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:0b6308635a44c1ebc146fdfa219eede183ae44ea1dc2d8a149d738fb0c291fce","observation_id":"510e7ac0-e063-4c76-b379-a7a27e57d0ab","resolution":{"observed_at":"2026-08-07T12:04:31.977080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:26.553695Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.553695Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:62dbc001cb509144ee9cb63d8cee70dbaaa88e0b9364de0f7211525267211591","observation_id":"d0405940-7955-4b13-8044-8ae232bb1cd7","resolution":{"observed_at":"2026-08-07T12:04:26.553695Z","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":"2026-08-07T12:04:26.645702Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.645702Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:62c8d03bf7ee2233853831a0fdbf535ef535abfe5a8d3e69463d9f192f45c060","observation_id":"85554446-e574-46d4-b38f-201bb03c130c","resolution":{"observed_at":"2026-08-07T12:04:26.645702Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:31.590728Z","title":"Plaat, Deep reinforcement learning","venue":null,"work_id":"b2a43f2f-5cde-4e6b-942c-5929c3a6da38","year":2022},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.737942Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:7d2be36011594a93cdafa505c33406f3034d0e94ca6d1896a4c767c7a8274b75","observation_id":"a6c0b4b8-1923-484d-8d2d-d984b1601a8b","resolution":{"observed_at":"2026-08-07T12:04:31.703416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:31.378573Z","title":"PeFLL: Personalized federated learning by learning to learn,","venue":null,"work_id":"e24f891e-a91b-42f2-be23-f90d97ab9032","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.820765Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:e4396a1a2e1bd6443b4c17aa1d0ce05785d6a454576f8f986e35b5f66b6fea15","observation_id":"2e0b3a71-c05a-4546-9772-d98cb2f7e185","resolution":{"observed_at":"2026-08-07T12:04:31.480021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:26.924809Z","title":"Reinforcement learning: An introduction,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:26.924809Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:4860fb2f1490ca4d3703f58e21ee21ef2ac7e7742579da83d36615fb8f45f129","observation_id":"2398d747-2b05-4882-ae4c-810cc9aa01d2","resolution":{"observed_at":"2026-08-07T12:04:26.924809Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:31.144228Z","title":"Fedimpro: Measuring and improving client update in federated learning,","venue":null,"work_id":"c519e7c7-e21d-471a-9052-099450af238d","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.046422Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:b83a62f197c78a20b162fa32e4c554a039d5decb5702ffdfe0ee13bc2ce269e1","observation_id":"b9938c67-2784-4465-b8d4-6849e5788a5b","resolution":{"observed_at":"2026-08-07T12:04:31.237544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:27.163598Z","title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.163598Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:cb711771386dd6a99de22957efb39ae16548b364a6232461e76077d905883330","observation_id":"a4525e4f-7355-493d-8927-dbeb0b25007c","resolution":{"observed_at":"2026-08-07T12:04:27.163598Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:30.946580Z","title":"Confidence region estimation techniques for nonlinear regression in groundwater flow: Three case studies,","venue":null,"work_id":"b8c84524-af55-4b56-9c6e-693827b3e832","year":2007},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.264835Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:c7993e6be76279dbb3865a0936fb924fd109d22c0d29d0d2c97490182ae9f108","observation_id":"8e005efc-49c3-4360-8f35-d8ec5de16acd","resolution":{"observed_at":"2026-08-07T12:04:31.006033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:30.700362Z","title":"FedCDA: Federated learning with cross-rounds divergence-aware aggregation,","venue":null,"work_id":"726a65c4-1fba-4842-8893-bcd7d0dabd37","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.371324Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:151f12e12ecf051f8258cf4c0a942a597dfb735c995b4a7106d523c36c78f46e","observation_id":"7e29d974-04ab-41a2-a73e-6caa9f8cda18","resolution":{"observed_at":"2026-08-07T12:04:30.799691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:30.498829Z","title":"Tackling the data heterogeneity in asynchronous federated learning with cached update calibration,","venue":null,"work_id":"36e65823-2e8f-4178-928b-b211142f98db","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.426466Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:6d13938e0918ddf34bf905337334c8c8af833d1237e89ad30920085c1a443107","observation_id":"320b8ca5-cdf7-46c7-b9cf-6e7590b2e69f","resolution":{"observed_at":"2026-08-07T12:04:30.572845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:30.317338Z","title":"Fedhyper: A universal and robust learning rate scheduler for federated learning with hypergradient descent,","venue":null,"work_id":"7833654b-39c1-4371-a142-cbd40a89df3b","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.527423Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:9c8837e26a872b0f967186acf35c7fe8a1353de5255ad4c07591aeeac835a936","observation_id":"a7af7ea8-7751-43ba-a09b-f84bc558e364","resolution":{"observed_at":"2026-08-07T12:04:30.376050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:30.115304Z","title":"Fedinverse: Evaluating privacy leakage in federated learning,","venue":null,"work_id":"857956d2-a53b-416b-9aa1-5aeb56f5b37b","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.626157Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:5e5791926d4dd6e362a961de1dce6114eb90f663e451da8e2ed94400a037bd36","observation_id":"86a7912f-e08e-4bd0-b700-659ad25ef7f7","resolution":{"observed_at":"2026-08-07T12:04:30.195382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:04:27.700272Z","title":"Personalized federated learning for intelligent iot applications: A cloud-edge based framework,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.700272Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:f721e8838c7a0dc372192748cfb69b380ff3e5d34d3062e5e40f120d9e12603e","observation_id":"d065f0ea-85fa-4ce2-a83e-1413ae86c2d7","resolution":{"observed_at":"2026-08-07T12:04:27.700272Z","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":"2026-08-07T12:04:27.770932Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.770932Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:596e86279f2ddc553c5da3f8e2089268de174fc7f867f8b023714c1fa6c359e1","observation_id":"4cc6b17c-f440-493a-b011-c6e85baf2db5","resolution":{"observed_at":"2026-08-07T12:04:27.770932Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:29.886361Z","title":"Trustworthy personalized bayesian federated learning via posterior fine-tune,","venue":null,"work_id":"65dc4558-a89d-485b-9076-4b18f8fe5165","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.855080Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:b10c00afd29a7455ea73779a541d9978d78e1210a2945d6bff546a2462d5a2df","observation_id":"dda8b980-6b7d-440a-abbe-a1decff6e096","resolution":{"observed_at":"2026-08-07T12:04:29.939261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:29.720693Z","title":"Byzantine-robust distributed learning: Towards optimal statistical rates,","venue":null,"work_id":"29edb96e-8b75-4b38-8bfb-aab5ae1fbe1a","year":2018},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:27.954236Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:4030335ca36707c73ce224061f0bc15ba5b623e96065c401eed66a828f262b5f","observation_id":"58d6f766-2bb3-49b8-9093-9f8acee3f7d9","resolution":{"observed_at":"2026-08-07T12:04:29.780302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:29.550334Z","title":"Exploiting class feature alignment for personalized federated learning in mixed skew scenarios,","venue":null,"work_id":"f6d926a7-d63b-435f-b8cc-67f91ac9cada","year":2024},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:28.053576Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:d470dff74f9d7daf1f1d07b9caf54996d5dde8e8f4e011b26542aaf5fed4f1cd","observation_id":"625a27f4-1fdd-4728-bfd3-2667a50385b6","resolution":{"observed_at":"2026-08-07T12:04:29.624242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:29.333058Z","title":"Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection,","venue":null,"work_id":"1eb2f299-9699-48ea-b092-f180e60b1d98","year":2023},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:28.171610Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:84a52a99d0539d2753bff4d26d5cdcc509b8eeba754dfb58d5f45dbf8f2ee233","observation_id":"6cfa0432-3c7c-4ca3-b430-9e91f5338dc8","resolution":{"observed_at":"2026-08-07T12:04:29.429409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:29.085045Z","title":"Bayesian nonparametric federated learning of neural networks,","venue":null,"work_id":"9b9f08cb-207f-48cd-b2ae-0ef66eb76ff2","year":2019},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:28.277698Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:9ee5cfa70cf88fb10f10c489e4bc3fab49671fd339a72bda358aa0981e228b91","observation_id":"08d4987c-5b0c-487e-b5bd-36600db19ffc","resolution":{"observed_at":"2026-08-07T12:04:29.183683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:04:28.892176Z","title":"Fedala: Adaptive local aggregation for personalized federated learning,","venue":null,"work_id":"1e371ee5-0647-4e60-b364-2d9f7ccf5f38","year":2023},"citing_paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:28.363345Z"},"links":{"citing_paper":"/paper/2506.06337"},"observation_digest":"sha256:ecf93a2abbaf3004f5eaa0b74292ec22dd56002ebe3edd94377c2a9f4979b5ea","observation_id":"940d2a3a-30f8-49ad-a64e-791c5defc227","resolution":{"observed_at":"2026-08-07T12:04:28.974150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.06337","last_updated":"2025-05-31T19:32:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T11:58:03.108781Z","submitted_at":"2025-05-31T19:32:42Z","title":"Optimized Local Updates in Federated Learning via Reinforcement Learning"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":31},"total_outbound_references":49},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2506.06337."}