{"as_of":"2026-08-10T01:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2ca62211db5e0c2dfa3bb1c3253a0ad5828e2aa89bacc0c7611654b474e1e782","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T11:35:29.665720Z","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":"2026-05-20T14:13:21.237419Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10940","snapshot_observed_at":"2026-08-08T11:35:29.665720Z","title":"Stochastic-sign sgd for federated learning with theoretical guarantees","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.07923","last_updated":"2025-05-27T15:31:20Z","snapshot_observed_at":"2026-08-08T23:26:23.316471Z","submitted_at":"2025-02-11T19:54:11Z","title":"Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T11:35:29.665720Z"},"links":{"cited_paper":"/paper/2002.10940","citing_paper":"/paper/2502.07923"},"observation_digest":"sha256:68ce6980edf8f1ff876ffdeea0cfda1deb6cdd7d5e3ad6566ef11a2693523c0c","observation_id":"18de87ef-29fd-40ad-a805-85677420daf0","resolution":{"observed_at":"2026-08-08T11:35:29.665720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10940","snapshot_observed_at":"2026-08-07T04:17:46.792095Z","title":"Stochastic-sign SGD for federated learning with the- oretical guarantees,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.11413","last_updated":"2025-06-13T02:23:41Z","snapshot_observed_at":"2026-08-09T00:58:55.513764Z","submitted_at":"2025-06-13T02:23:41Z","title":"Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:46.792095Z"},"links":{"cited_paper":"/paper/2002.10940","citing_paper":"/paper/2506.11413"},"observation_digest":"sha256:424595434ba2c67e53f0818a085c9af013ab505898404301174aa500b0227400","observation_id":"e443549c-b651-4ca3-9450-c1c3f949e365","resolution":{"observed_at":"2026-08-07T04:17:46.792095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10940","snapshot_observed_at":"2026-08-06T17:13:44.717529Z","title":null,"venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.12091","last_updated":"2025-07-16T09:54:08Z","snapshot_observed_at":"2026-08-09T00:59:12.919541Z","submitted_at":"2025-07-16T09:54:08Z","title":"Improved Analysis for Sign-based Methods with Momentum Updates","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T17:13:44.717529Z"},"links":{"cited_paper":"/paper/2002.10940","citing_paper":"/paper/2507.12091"},"observation_digest":"sha256:fc02a5f90abb6a6088cd320ce6642d1b207d8abd2fa31fe5377012677b9627d9","observation_id":"6c20472c-8c0f-4fdf-93ef-71f74f3ce58f","resolution":{"observed_at":"2026-08-06T17:13:44.717529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10940","snapshot_observed_at":"2026-08-03T20:29:49.125473Z","title":"Stochastic-sign SGD for federated learning with theoretical guarantees.arXiv preprint arXiv:2002.10940, 2020","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2511.19959","last_updated":"2026-06-02T00:32:45Z","snapshot_observed_at":"2026-08-03T21:43:59.986677Z","submitted_at":"2025-11-25T06:09:21Z","title":"ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T20:29:49.125473Z"},"links":{"cited_paper":"/paper/2002.10940","citing_paper":"/paper/2511.19959"},"observation_digest":"sha256:8ee135e64a4e6dcfc5fde06158746a5405e670a0c7b9cd62d38e4bae65a566fc","observation_id":"9a82bb99-8359-499e-a71a-0864560b9994","resolution":{"observed_at":"2026-08-03T20:29:49.125473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":"2002.10940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.10940","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c98abf9c-3894-4938-b900-191e3f900ff7","year":2020},"citing_paper":{"arxiv_id":"2605.17552","last_updated":"2026-05-17T17:23:23Z","snapshot_observed_at":"2026-07-06T23:28:30.942078Z","submitted_at":"2026-05-17T17:23:23Z","title":"Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-20T14:11:53.371521Z"},"links":{"cited_paper":"/paper/2002.10940","citing_paper":"/paper/2605.17552"},"observation_digest":"sha256:4decfdfae954cbc5e44e6c7eadd8a21c870e5f6b575b99923d03a80445006622","observation_id":"746ed81a-b507-4d46-8694-eb50005040d2","resolution":{"observed_at":"2026-05-20T14:13:21.238755Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2002.10940/citation-record","integrity":"/paper/2002.10940/integrity","json":"/paper/2002.10940/citation-record.json","paper":"/paper/2002.10940"},"outbound":[],"paper":{"arxiv_id":"2002.10940","last_updated":"2021-09-27T21:16:21Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T16:30:02.185904Z","submitted_at":"2020-02-25T15:12:15Z","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2002.10940."}