{"as_of":"2026-08-22T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f58c1fd055ab783a666c2e1bce8962a2159c690f8339d93a0d16035268def44","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:46:47.779439Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2501.13790/citation-record","integrity":"/paper/2501.13790/integrity","json":"/paper/2501.13790/citation-record.json","paper":"/paper/2501.13790"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:46:48.492519Z","title":"Communication complexity of distributed convex learning and optimization","venue":null,"work_id":"76c86e07-a932-4c1d-8a2d-def997e57218","year":2015},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.547875Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:d4e7b276b316a2ba335beba193fe3ce87d13a4ab1fc13e6819822be90d3906ba","observation_id":"a37cd258-0359-4903-94ac-22a3b6a7f09f","resolution":{"observed_at":"2026-08-10T15:46:48.497755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.476275Z","title":"Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression","venue":null,"work_id":"c85e678b-5b95-4d81-915c-4346b348617f","year":2014},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.553915Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:dbc1274f70c91a25b6531968f35b6261768727ec0d8c8fd5fc898e2a9d452a95","observation_id":"14e2327a-b59b-46a0-a82d-b70593fb9fc1","resolution":{"observed_at":"2026-08-10T15:46:48.481154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.460588Z","title":"Distributed learning, communication complexity and privacy","venue":null,"work_id":"588e7464-734f-47cf-95ae-2d02e4904e6c","year":2012},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.559373Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:d56798ced2d3426d48de40d8dc1bc35d3b3143d868393d0a4d63855bfc5000b8","observation_id":"7995ca07-4999-47ab-9ebe-20f901db577e","resolution":{"observed_at":"2026-08-10T15:46:48.465943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.443366Z","title":"Gradient descent on neural networks typically occurs at the edge of stability","venue":null,"work_id":"f0eac7db-8ab5-44d8-9ffc-3224162d217e","year":2021},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.566003Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:17972ab7495d7a3dde67f1ee3232d1bc28bd9c48cfe5742928c23805f1231918","observation_id":"c6b5e213-be08-4ffe-ace2-cf483f29e582","resolution":{"observed_at":"2026-08-10T15:46:48.448354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.428284Z","title":"Optimal distributed online prediction using mini-batches","venue":null,"work_id":"18fe1b5a-307b-4b78-b13d-41b0df574914","year":2012},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.571559Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:2d81b0b1333535cd6f7d2cd3190449b2828473323cbef452a3349d6686ac8f74","observation_id":"5f460f20-dc5c-4b94-8209-12d2be8c0ff0","resolution":{"observed_at":"2026-08-10T15:46:48.433228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.576742Z","title":"Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.576742Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:8daabce3f84d07498b9db907c3bbf8e161ee3a1536adfffeaea472065840d117","observation_id":"1464e2d9-3b0d-4262-9578-506d6912199c","resolution":{"observed_at":"2026-08-10T15:46:47.576742Z","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":"2026-08-10T15:46:48.401695Z","title":"Sharp bounds for federated averaging (local sgd) and continuous perspective","venue":null,"work_id":"1a2b55fe-0884-4579-9570-f801c6438249","year":2022},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.582207Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:dc4cf9a832c3952d26c36b649cd5bbd3f930cf7079c4d1dd71c06947d4561bae","observation_id":"72110b65-9ec8-4a0b-8045-b9ec7801e4ee","resolution":{"observed_at":"2026-08-10T15:46:48.407281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.385190Z","title":"Characterizing implicit bias in terms of optimization geometry","venue":null,"work_id":"e6f38102-545d-41f7-82bd-0f2a2677d78f","year":2018},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.587445Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:f25be1bedabe2a2b29ca72819a19d57aaadc4a0ce28673808126de1f82d97442","observation_id":"9775e904-e1c6-49cf-933b-fa760af9f3db","resolution":{"observed_at":"2026-08-10T15:46:48.390148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.14425","last_updated":"2019-12-06T21:16:41Z","snapshot_observed_at":"2026-08-19T16:29:45.251659Z","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":"2026-08-10T15:46:47.592529Z","title":"On the convergence of local descent methods in federated learning","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.592529Z"},"links":{"cited_paper":"/paper/1910.14425","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:f795a97e5d870f4200a426f0705a3eefedd2aa475f9a2db6a912cfe7c76d2904","observation_id":"5735e215-e8a2-4bc7-b6c9-ab02ee3637a4","resolution":{"observed_at":"2026-08-10T15:46:47.592529Z","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":"2026-08-10T15:46:47.598119Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.598119Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:72d87a8ba70307aa8d390c00b14e5fc2f8877aa3f408bedafc72159be9f2afb8","observation_id":"f899152c-e320-4cce-96d6-28bf9fec06b1","resolution":{"observed_at":"2026-08-10T15:46:47.598119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07300","last_updated":"2019-06-08T13:57:05Z","snapshot_observed_at":"2026-08-18T09:56:39.606239Z","submitted_at":"2018-03-20T08:47:27Z","title":"Risk and parameter convergence of logistic regression","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07300","snapshot_observed_at":"2026-08-10T15:46:47.602767Z","title":"Risk and parameter convergence of logistic regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.602767Z"},"links":{"cited_paper":"/paper/1803.07300","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:b29aee0dd70ab7d61c11100db5c7cb2681dfe96babb5cfe730dd22343ec5ece5","observation_id":"b2c9ffab-6320-4584-a086-e916b71a501e","resolution":{"observed_at":"2026-08-10T15:46:47.602767Z","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":"2026-08-10T15:46:48.360237Z","title":"Fast margin maximization via dual acceleration","venue":null,"work_id":"7dea4ecb-3200-4813-9eff-40a0cc89bdfe","year":2021},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.607982Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:61c21652f827eb578e63541c2f32963d7da892b027e99765ce901da0d97f22b9","observation_id":"242c0e72-40cd-49f0-a785-47ef71b66b3e","resolution":{"observed_at":"2026-08-10T15:46:48.365136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.04977","last_updated":"2021-03-09T03:03:49Z","snapshot_observed_at":"2026-08-13T11:40:49.533339Z","submitted_at":"2019-12-10T20:55:41Z","title":"Advances and Open Problems in Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.04977","snapshot_observed_at":"2026-08-10T15:46:47.612502Z","title":"Advances and open problems in federated learning","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.612502Z"},"links":{"cited_paper":"/paper/1912.04977","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:9d77330d080ac2cce8245d1256f56519084c8908e8570c64532b25e663a9696f","observation_id":"3a99c2c4-1754-4a19-adb6-94d178ce570e","resolution":{"observed_at":"2026-08-10T15:46:47.612502Z","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":"2026-08-10T15:46:47.617690Z","title":"Advances and open problems in federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.617690Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:f526398820fa38445ce0dbaf4be8c8d7d419218f2ac888d18ad3195c9846f47e","observation_id":"6b6cf274-2572-496d-bf60-7e666961a3d0","resolution":{"observed_at":"2026-08-10T15:46:47.617690Z","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":"2026-08-10T15:46:47.622481Z","title":"Scaffold: Stochastic controlled averaging for federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.622481Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:c64bfe2842784c124253da3c915da19cf4aa722847be06e7ad2d397682fb6f8f","observation_id":"69e8a5cd-4fa6-47a3-a9fc-f8fb23fe347d","resolution":{"observed_at":"2026-08-10T15:46:47.622481Z","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":"2026-08-10T15:46:48.320914Z","title":"Tighter theory for local sgd on identical and heterogeneous data","venue":null,"work_id":"c4212901-6ac5-41ae-9b9b-5a4615fd4e95","year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.627218Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:8a5d40aeabd955fee21cd5d51f50fa85a94b89f8be32821f5a8df9b5023ee6d0","observation_id":"8ac92962-002d-49d2-8b24-a9656cc158c8","resolution":{"observed_at":"2026-08-10T15:46:48.327498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.305751Z","title":"A unified theory of decentralized sgd with changing topology and local updates","venue":null,"work_id":"a9004425-1516-4416-bb44-2d5868d936d1","year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.632412Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:47abbbfa67cebf1ef81d318c529d3cd969d2058befa00d99f39e795d7a0bbe12","observation_id":"cfcc2ec2-8ecb-48bb-9d7d-a24906acdd30","resolution":{"observed_at":"2026-08-10T15:46:48.310600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.04169","last_updated":"2025-01-18T20:58:21Z","snapshot_observed_at":"2026-08-16T15:41:47.137638Z","submitted_at":"2023-04-09T06:10:49Z","title":"SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization","version":2},"cited_work":{"arxiv_id":"2304.04169","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.04169","snapshot_observed_at":"2026-08-10T15:46:47.881798Z","title":"SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization","venue":"cs.LG","work_id":"5e89c082-c8ed-46ff-a779-84136373c177","year":2023},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.637050Z"},"links":{"cited_paper":"/paper/2304.04169","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:b109baad09a4a8d9102a45db779ab94fdbd11e7df65a5fc432e130ce7b768afa","observation_id":"dc729fa8-7f92-4226-a8fc-6e9231dcd9d0","resolution":{"observed_at":"2026-08-10T15:46:47.889279Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.290249Z","title":"Don't use large mini-batches, use local sgd","venue":null,"work_id":"2aa6f3a8-f0bd-4745-a7bb-3fbb968772cb","year":2019},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.642171Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:f9fe7f6c51d14a005209f6d54696d9346aa0f42bd4b12ebfb0ede7d229bd160a","observation_id":"3f0cec6c-dcf2-4ebd-85d7-d1072a72e2b6","resolution":{"observed_at":"2026-08-10T15:46:48.295099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.274519Z","title":"Efficient large-scale distributed training of conditional maximum entropy models","venue":null,"work_id":"02a7c53b-edbe-4146-a5fd-536c8f414e7b","year":2009},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.647041Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:9fc495f04c0162b167ba370f941048b79a7dd3961b0ec8c2c365008bad03781f","observation_id":"aa8eaeea-d511-4d82-bf74-fb571c48af1b","resolution":{"observed_at":"2026-08-10T15:46:48.279589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.258831Z","title":"Distributed training strategies for the structured perceptron","venue":null,"work_id":"9921c1a1-8e92-464a-8568-66c797c041d6","year":2010},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.651852Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:efd7a7612ef032d247d0afdf998b832967d4ae4e52c508786cdb8f5d51727e62","observation_id":"96cd270d-3102-41be-a050-7891f00f85a0","resolution":{"observed_at":"2026-08-10T15:46:48.264210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.656728Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.656728Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:9a65a96dcd92d348513510323358dca1934331704121929ec39505e1f627eb80","observation_id":"20ae09e3-7989-40c6-a7e7-f26d405b2170","resolution":{"observed_at":"2026-08-10T15:46:47.656728Z","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":"2026-08-10T15:46:48.233024Z","title":"Proximal and federated random reshuffling","venue":null,"work_id":"96be3289-44d0-424c-9696-da817c695154","year":2022},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.661732Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:3089063e70ae411305826caaa1f3a6f07c3ae801e56582df1b84e189366f5e68","observation_id":"64a299ee-01aa-440a-8d6b-69a7f7df0c93","resolution":{"observed_at":"2026-08-10T15:46:48.239008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.217432Z","title":"Stochastic gradient descent on separable data: Exact convergence with a fixed learning rate","venue":null,"work_id":"b71eb7ef-ac89-4ba8-980b-76163ea3d06c","year":2019},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.667587Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:82402005160cc164647b8732e491d8d6e5efa3c8f43144ae35a7dd15c699ef6d","observation_id":"4ee241fb-8002-4e79-9b36-030cd3ccccd8","resolution":{"observed_at":"2026-08-10T15:46:48.222332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.672305Z","title":"Introductory lectures on convex optimization: A basic course, volume 87","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.672305Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:4738270e92c5129bd1bcb7b27a4740105153e3c5c630002e4d3cf913debcde6a","observation_id":"974679a0-77bd-401d-8225-5c21af772912","resolution":{"observed_at":"2026-08-10T15:46:47.672305Z","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":"2026-08-10T15:46:48.191837Z","title":"A minimizer far, far away, 2024","venue":null,"work_id":"f40ed645-70da-419e-9706-2cf5ad9ca0b7","year":2024},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.677037Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:c2cc9f100d09caf94f74214bedcf2e2d43e20255320179966a9863f6e685c687","observation_id":"bfe2ed56-1f9b-4ae9-9c98-8d512f74c33e","resolution":{"observed_at":"2026-08-10T15:46:48.196880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.175071Z","title":"On the still unreasonable effectiveness of federated averaging for heterogeneous distributed learning","venue":null,"work_id":"ad112a53-2fd7-40d3-a574-1cbe7aa74608","year":2023},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.682248Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:9c50d5a5c8e13770bc4df9f52aca34def8a31c1f3d872d2a0926f9eea3b25013","observation_id":"25efdabe-0ee7-4f86-9977-fd8b358108a6","resolution":{"observed_at":"2026-08-10T15:46:48.180318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.158324Z","title":"The limits and potentials of local sgd for distributed heterogeneous learning with intermittent communication","venue":null,"work_id":"33be37f2-8a76-4bfc-b891-2c7a13f1d12a","year":2024},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.687037Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:6f6866b3844753e69815cb6205563dd1db2305d395adcbc4145aacd404a86855","observation_id":"10bbef97-0329-4b30-b7dc-b2e5323eb478","resolution":{"observed_at":"2026-08-10T15:46:48.163374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.141118Z","title":"Distributed stochastic optimization and learning","venue":null,"work_id":"834f88a5-be08-4f26-9491-8ef8aefd48ca","year":2014},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.691757Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:743114ce7f4ce31a3996194794ffa1804f801f858b8e6b23f84e4f28dc0996ac","observation_id":"f9867f78-3f7c-4625-b054-5fa0441dbce3","resolution":{"observed_at":"2026-08-10T15:46:48.147628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.125835Z","title":"The implicit bias of gradient descent on separable data","venue":null,"work_id":"f6fd169e-30cd-4786-b168-5922795467e9","year":2018},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.696254Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:c87b11bcad31eb6c175572f9ca547638488c25fc1da77ac5f2a0874aeef8577a","observation_id":"564d3391-36df-4ad6-89ea-b3bdd871161c","resolution":{"observed_at":"2026-08-10T15:46:48.130857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.09767","last_updated":"2019-05-03T12:58:04Z","snapshot_observed_at":"2026-08-14T19:11:29.708396Z","submitted_at":"2018-05-24T16:38:51Z","title":"Local SGD Converges Fast and Communicates Little","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.09767","snapshot_observed_at":"2026-08-10T15:46:47.700925Z","title":"Local sgd converges fast and communicates little","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.700925Z"},"links":{"cited_paper":"/paper/1805.09767","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:2c4b75ed9a331cfc887b3498583de6ee5b7f5810c683ac573bb35768c4958200","observation_id":"f323f905-a048-488b-bfd0-aa2ff7383e44","resolution":{"observed_at":"2026-08-10T15:46:47.700925Z","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":"2026-08-10T15:46:48.110039Z","title":"Local sgd converges fast and communicates little","venue":null,"work_id":"3520232d-6ef8-449e-bfbe-b7fa50607e6a","year":2019},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.705844Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:b6e2ee060d7a00747e45f16555fccb0a7096bc39fc61062d56eddb89e9dff9f7","observation_id":"464b7f8b-05a1-4998-8e1a-153c4fc2915b","resolution":{"observed_at":"2026-08-10T15:46:48.115350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.06917","last_updated":"2021-07-14T18:09:08Z","snapshot_observed_at":"2026-08-16T18:09:59.877001Z","submitted_at":"2021-07-14T18:09:08Z","title":"A Field Guide to Federated Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.06917","snapshot_observed_at":"2026-08-10T15:46:47.710477Z","title":"A field guide to federated optimization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.710477Z"},"links":{"cited_paper":"/paper/2107.06917","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:8bf0872221ccddcd72980a107d81d74e52580ed3fece30556f54c9f5b24a940e","observation_id":"7a377e9c-0abb-4518-8895-df6bdfd23892","resolution":{"observed_at":"2026-08-10T15:46:47.710477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04723","last_updated":"2022-06-09T18:25:25Z","snapshot_observed_at":"2026-08-16T16:54:04.688135Z","submitted_at":"2022-06-09T18:25:25Z","title":"On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04723","snapshot_observed_at":"2026-08-10T15:46:47.715859Z","title":"On the unreasonable effectiveness of federated averaging with heterogeneous data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.715859Z"},"links":{"cited_paper":"/paper/2206.04723","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:224e90867ef96318c2cb984ccf7a9ffdfbdc1e6f0488f4224770646c782c7355","observation_id":"17fcfc90-36da-4d52-a850-ee1730236330","resolution":{"observed_at":"2026-08-10T15:46:47.715859Z","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":"2026-08-10T15:46:48.093834Z","title":"Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pp.\\ 10334--10343","venue":null,"work_id":"288eed29-4e80-4451-988d-c51ae3e6bbc0","year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.720585Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:37e9a0a4655eca11a7069c6cff99ea2e6da0e27e15fbb0e8dab4dee371ee4dc3","observation_id":"13d71d0d-e820-4a32-9a49-338c6da5da02","resolution":{"observed_at":"2026-08-10T15:46:48.098901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.726644Z","title":"Graph oracle models, lower bounds, and gaps for parallel stochastic optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.726644Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:405165ae5b075f7de60d3210cf0ad34407c924b72ab69f047dc6deee6f2edeb8","observation_id":"b1f52b1e-22fb-49fd-973e-1aae0fdfd9cb","resolution":{"observed_at":"2026-08-10T15:46:47.726644Z","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":"2026-08-10T15:46:48.064955Z","title":"Minibatch vs local sgd for heterogeneous distributed learning","venue":null,"work_id":"596daf98-dcd2-43b6-a47d-c93782166ecb","year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.731134Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:18832ad587631bc7bc9d49283320d759b7eb2a7811793357d816a5ed4bcb8054","observation_id":"cf8eb43e-7e2d-4ed7-86dc-ce8eec676e01","resolution":{"observed_at":"2026-08-10T15:46:48.070822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.047587Z","title":"The min-max complexity of distributed stochastic convex optimization with intermittent communication","venue":null,"work_id":"b00ec9b8-4e2f-4f99-a68d-9adb12737bcd","year":2021},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.735592Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:a6a370f3bc0ad63a77ae1aa0a09e6eb7ab8621e411195058a93530107265bb4e","observation_id":"21cd2931-a870-434a-9d94-9675a0976255","resolution":{"observed_at":"2026-08-10T15:46:48.053068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15926","last_updated":"2024-06-10T03:32:05Z","snapshot_observed_at":"2026-08-20T07:32:04.845020Z","submitted_at":"2024-02-24T23:10:28Z","title":"Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15926","snapshot_observed_at":"2026-08-10T15:46:47.740292Z","title":"Large stepsize gradient descent for logistic loss: Non-monotonicity of the loss improves optimization efficiency","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.740292Z"},"links":{"cited_paper":"/paper/2402.15926","citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:c9b0ccb2adced94cc0b34fb83fa2b1320c016894ac7841113156edf05bd31564","observation_id":"5f3e58ba-060e-4578-9d32-bba7b4217b6c","resolution":{"observed_at":"2026-08-10T15:46:47.740292Z","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":"2026-08-10T15:46:48.031871Z","title":"Implicit bias of gradient descent for logistic regression at the edge of stability","venue":null,"work_id":"2454370b-83e9-4ed6-abf1-eed61ea19ca5","year":2024},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.745231Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:b74d00ee0499725718ef935a81aecb0ca2440d5b9fdd92870118ed66af29face","observation_id":"77fe3839-79f6-4fc3-afc3-78cd4a5a9f29","resolution":{"observed_at":"2026-08-10T15:46:48.037192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:48.016229Z","title":"Federated accelerated stochastic gradient descent","venue":null,"work_id":"c94495aa-409e-48ee-9380-33efe2c6c43b","year":2020},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.749965Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:3118dd59e581c97a1454abc6880877a83304b514a5553f93d9872aea0c49d278","observation_id":"52e2efb2-1d9f-4873-bd65-a3dec788e6e9","resolution":{"observed_at":"2026-08-10T15:46:48.021582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.999996Z","title":"Information-theoretic lower bounds for distributed statistical estimation with communication constraints","venue":null,"work_id":"57920db0-0eb8-4832-96e0-d1967cfa2adb","year":2013},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.754451Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:c2c9bae707c579b5d24f05b5733c7ee83d2676de1ab8ce6868c11fa8a8a72a05","observation_id":"4b77c0b8-8d0c-4336-8b34-b57cdf1cc861","resolution":{"observed_at":"2026-08-10T15:46:48.005323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T15:46:47.758944Z","title":"Parallelized stochastic gradient descent","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.758944Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:a5421b754ef264857290a63abe6e2b62b258291318eaa29459b869308bd17f1c","observation_id":"ab1d4959-65a6-4dc9-a913-ee131a5b127e","resolution":{"observed_at":"2026-08-10T15:46:47.758944Z","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":"2026-08-10T15:46:47.764016Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.764016Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:4ab185922154d452fa74eb1d01ee5587f8531c9be03b77bcd8abed7ecd850ab2","observation_id":"2850221d-1a0f-49c3-b126-61a33c5ac142","resolution":{"observed_at":"2026-08-10T15:46:47.764016Z","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":"2026-08-10T15:46:47.769809Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.769809Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:938e0d8942c26e4c2dff8011114bd8350ab8ab735a4e9c96387b21fc876e0172","observation_id":"ab9a25ce-ec6d-44ad-8bbf-1353bd0d166e","resolution":{"observed_at":"2026-08-10T15:46:47.769809Z","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":"2026-08-10T15:46:47.774739Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.774739Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:2b92704b7e9e28cfb70fa34d248dcbd8d7a956c58c3afe1425e0d4026182c59c","observation_id":"9ba25018-7fca-4301-b6f8-80f5a964ad88","resolution":{"observed_at":"2026-08-10T15:46:47.774739Z","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":"2026-08-10T15:46:47.779439Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T15:46:47.779439Z"},"links":{"citing_paper":"/paper/2501.13790"},"observation_digest":"sha256:2c7921f22efe69a983f8fb637d2c5ac226efad21bcd8b27a84a54e72f2422f86","observation_id":"c3b30e2b-313e-4239-83d7-e625c6d3e92a","resolution":{"observed_at":"2026-08-10T15:46:47.779439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.13790","last_updated":"2025-05-07T15:34:15Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T23:29:23.810814Z","submitted_at":"2025-01-23T16:09:26Z","title":"Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":27},"total_outbound_references":47},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.13790."}