{"as_of":"2026-08-12T05:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f30db4b15debbea6314123ca32402117c1aa34a0870e330b614603ec8aab33f","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T19:54:41.604681Z","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":"2026-06-29T00:12:50.697819Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-08-09T19:54:41.604681Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.00206","last_updated":"2025-01-31T22:48:43Z","snapshot_observed_at":"2026-08-09T19:46:34.246723Z","submitted_at":"2025-01-31T22:48:43Z","title":"BICompFL: Stochastic Federated Learning with Bi-Directional Compression","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T19:54:41.604681Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2502.00206"},"observation_digest":"sha256:f97833bf9cfed08ed9ddedd1639329eef5ff7e8e08789233c050fba71ff58888","observation_id":"3f2e247e-b4ff-40a3-b8e9-d6907decc848","resolution":{"observed_at":"2026-08-09T19:54:41.604681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-08-04T21:06:26.439936Z","title":"Philippenko and A","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.08233","last_updated":"2025-09-10T02:19:56Z","snapshot_observed_at":"2026-08-06T06:55:03.018292Z","submitted_at":"2025-09-10T02:19:56Z","title":"Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization","version":1},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-08-04T21:06:26.439936Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2509.08233"},"observation_digest":"sha256:e200845c4a06f3d601c36753bb99664d2cafe93ea073b49db3a6ed1905b65051","observation_id":"0e6b6b8f-4b7a-49eb-a9b6-d5cb083028f2","resolution":{"observed_at":"2026-08-04T21:06:26.439936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":"2006.14591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-06-29T00:12:50.697819Z","title":"arXiv preprint arXiv:2006.14591 , year=","venue":null,"work_id":"aaf2f1fb-96c5-4524-922e-e0df0f223c6e","year":2006},"citing_paper":{"arxiv_id":"2605.07795","last_updated":"2026-05-08T14:32:41Z","snapshot_observed_at":"2026-07-06T23:20:06.574823Z","submitted_at":"2026-05-08T14:32:41Z","title":"Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits","version":1},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-05-11T02:52:53.588595Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2605.07795"},"observation_digest":"sha256:c660e81f1b8e35086910cc7e2105452ef57a788dd9b3e4ac04d7916771eebe62","observation_id":"828a48a8-3c9e-48e7-8f90-a8832e20e5f4","resolution":{"observed_at":"2026-05-11T03:05:54.904432Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":"2006.14591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-06-29T00:12:50.697819Z","title":"arXiv preprint arXiv:2006.14591 , year=","venue":null,"work_id":"aaf2f1fb-96c5-4524-922e-e0df0f223c6e","year":2006},"citing_paper":{"arxiv_id":"2605.08871","last_updated":"2026-05-09T10:46:59Z","snapshot_observed_at":"2026-08-11T12:27:08.580370Z","submitted_at":"2026-05-09T10:46:59Z","title":"Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction","version":1},"reference_index":187,"source":"arxiv_source","source_observed_at":"2026-05-12T01:51:20.003552Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2605.08871"},"observation_digest":"sha256:1bda8047649837551f6d76aad252b8053471fa7dd1e9e155427404a8945a8c50","observation_id":"4cc05a88-27aa-474f-9872-26c1a55c6c2d","resolution":{"observed_at":"2026-05-12T07:51:33.116980Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":"2006.14591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-06-29T00:12:50.697819Z","title":"arXiv preprint arXiv:2006.14591 , year=","venue":null,"work_id":"aaf2f1fb-96c5-4524-922e-e0df0f223c6e","year":2006},"citing_paper":{"arxiv_id":"2605.18174","last_updated":"2026-05-18T10:18:02Z","snapshot_observed_at":"2026-07-06T23:29:04.241654Z","submitted_at":"2026-05-18T10:18:02Z","title":"Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method","version":1},"reference_index":189,"source":"arxiv_source","source_observed_at":"2026-05-20T13:08:52.912250Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2605.18174"},"observation_digest":"sha256:e26c87d07cc766c651e047cf66500180b7bbe75377aceda1c352fadae50833d8","observation_id":"1a874812-1234-4bda-87d2-e8bda589862b","resolution":{"observed_at":"2026-05-20T13:13:18.609560Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":"2006.14591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-06-29T00:12:50.697819Z","title":"arXiv preprint arXiv:2006.14591 , year=","venue":null,"work_id":"aaf2f1fb-96c5-4524-922e-e0df0f223c6e","year":2006},"citing_paper":{"arxiv_id":"2605.20866","last_updated":"2026-05-20T08:01:45Z","snapshot_observed_at":"2026-08-12T03:08:02.214097Z","submitted_at":"2026-05-20T08:01:45Z","title":"LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging","version":1},"reference_index":190,"source":"arxiv_source","source_observed_at":"2026-05-21T05:49:28.713982Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2605.20866"},"observation_digest":"sha256:b29412f6c1163319dbb884f584b258d2725f72c0032a9fb18949bf6394fe85ce","observation_id":"d3f9f975-659a-4e5b-90a9-ef4267941095","resolution":{"observed_at":"2026-05-21T05:49:40.539745Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","version":4},"cited_work":{"arxiv_id":"2006.14591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14591","snapshot_observed_at":"2026-06-29T00:12:50.697819Z","title":"arXiv preprint arXiv:2006.14591 , year=","venue":null,"work_id":"aaf2f1fb-96c5-4524-922e-e0df0f223c6e","year":2006},"citing_paper":{"arxiv_id":"2605.31594","last_updated":"2026-05-29T17:57:03Z","snapshot_observed_at":"2026-08-08T11:48:16.043515Z","submitted_at":"2026-05-29T17:57:03Z","title":"A Tight Theory of Error Feedback Algorithms in Distributed Optimization","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-28T23:07:05.700294Z"},"links":{"cited_paper":"/paper/2006.14591","citing_paper":"/paper/2605.31594"},"observation_digest":"sha256:bbd6c94b414d78deb7114abf860c7f24673777fd911cded88c82423f4c36123f","observation_id":"69ee2904-0019-4113-9c7d-578e88cf9b51","resolution":{"observed_at":"2026-06-29T00:12:50.701309Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.14591/citation-record","integrity":"/paper/2006.14591/integrity","json":"/paper/2006.14591/citation-record.json","paper":"/paper/2006.14591"},"outbound":[],"paper":{"arxiv_id":"2006.14591","last_updated":"2022-06-19T15:40:37Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T03:09:42.476215Z","submitted_at":"2020-06-25T17:37:45Z","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence 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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2006.14591."}