{"as_of":"2026-08-08T18:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5b7d5bcbe9677e6aa9afb97f353418f9b367c61e683b84ff4be8de4a6439cc0d","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:45:34.890436Z","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":"2026-05-23T23:48:39.322523Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.14937","last_updated":"2021-06-16T16:30:15Z","snapshot_observed_at":"2026-08-03T21:21:39.057212Z","submitted_at":"2021-04-30T12:02:03Z","title":"Federated Learning with Fair Averaging","version":5},"cited_work":{"arxiv_id":"2104.14937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.14937","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated learning with fair averaging","venue":null,"work_id":"2e4f58cf-8b11-45c7-bb3f-3d24072f2d0d","year":2021},"citing_paper":{"arxiv_id":"2406.10861","last_updated":"2024-06-16T09:12:16Z","snapshot_observed_at":"2026-07-06T18:31:40.910349Z","submitted_at":"2024-06-16T09:12:16Z","title":"Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions","version":1},"reference_index":157,"source":"pdf_text","source_observed_at":"2026-05-23T23:47:28.874336Z"},"links":{"cited_paper":"/paper/2104.14937","citing_paper":"/paper/2406.10861"},"observation_digest":"sha256:63c620f83c69e1f2af93c66ff616bb0084be48bb6e14d70a03e2f8ad1e734201","observation_id":"a56dceee-081e-424a-964c-1f95c192bff0","resolution":{"observed_at":"2026-05-23T23:48:39.326092Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14937","last_updated":"2021-06-16T16:30:15Z","snapshot_observed_at":"2026-08-03T21:21:39.057212Z","submitted_at":"2021-04-30T12:02:03Z","title":"Federated Learning with Fair Averaging","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14937","snapshot_observed_at":"2026-08-07T12:45:34.890436Z","title":"Federated learning with fair averaging,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23588","last_updated":"2025-05-29T16:00:34Z","snapshot_observed_at":"2026-08-07T12:40:53.209321Z","submitted_at":"2025-05-29T16:00:34Z","title":"Accelerated Training of Federated Learning via Second-Order Methods","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:34.890436Z"},"links":{"cited_paper":"/paper/2104.14937","citing_paper":"/paper/2505.23588"},"observation_digest":"sha256:985290245116c65bb9a987a2d578cede9dbcf5fecbf5f39f37fc3c4d914c51aa","observation_id":"8e889ea8-c40a-4316-ad23-d16f1202447f","resolution":{"observed_at":"2026-08-07T12:45:34.890436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14937","last_updated":"2021-06-16T16:30:15Z","snapshot_observed_at":"2026-08-03T21:21:39.057212Z","submitted_at":"2021-04-30T12:02:03Z","title":"Federated Learning with Fair Averaging","version":5},"cited_work":{"arxiv_id":"2104.14937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.14937","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated learning with fair averaging","venue":null,"work_id":"2e4f58cf-8b11-45c7-bb3f-3d24072f2d0d","year":2021},"citing_paper":{"arxiv_id":"2512.12022","last_updated":"2026-04-17T23:33:06Z","snapshot_observed_at":"2026-07-06T22:38:54.914862Z","submitted_at":"2025-12-12T20:30:11Z","title":"DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-16T22:33:50.216120Z"},"links":{"cited_paper":"/paper/2104.14937","citing_paper":"/paper/2512.12022"},"observation_digest":"sha256:7b8b5583ef16538c026d791e7c304bea80bd21a551570040c0934662112c9753","observation_id":"c44a96fc-8591-4a6d-9483-bb6eeca2827e","resolution":{"observed_at":"2026-05-16T22:38:37.912075Z","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/2104.14937/citation-record","integrity":"/paper/2104.14937/integrity","json":"/paper/2104.14937/citation-record.json","paper":"/paper/2104.14937"},"outbound":[],"paper":{"arxiv_id":"2104.14937","last_updated":"2021-06-16T16:30:15Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T21:21:39.057212Z","submitted_at":"2021-04-30T12:02:03Z","title":"Federated Learning with Fair Averaging"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2104.14937."}