{"as_of":"2026-08-15T02:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:985ee012e5fd82483cbf7df2791671b72fa45764447b44a98007ff5d498ff13c","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:39:18.554569Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T18:34:19.135460Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2112.13246","last_updated":"2023-02-20T04:10:03Z","snapshot_observed_at":"2026-08-14T05:37:01.513042Z","submitted_at":"2021-12-25T14:58:52Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13246","snapshot_observed_at":"2026-08-12T17:39:18.554569Z","title":"Towards federated learning on time- evolving heterogeneous data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.12377","last_updated":"2024-12-12T18:16:23Z","snapshot_observed_at":"2026-08-14T21:24:35.072180Z","submitted_at":"2024-11-19T09:53:28Z","title":"Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions","version":2},"reference_index":124,"source":"pdf_text","source_observed_at":"2026-08-12T17:39:18.554569Z"},"links":{"cited_paper":"/paper/2112.13246","citing_paper":"/paper/2411.12377"},"observation_digest":"sha256:919631ec3d4047c0d740b975a08b977dfab939e7a52c7493d69f432104a63beb","observation_id":"83fa488d-cd27-4fca-81e8-6f635084cf9b","resolution":{"observed_at":"2026-08-12T17:39:18.554569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13246","last_updated":"2023-02-20T04:10:03Z","snapshot_observed_at":"2026-08-14T05:37:01.513042Z","submitted_at":"2021-12-25T14:58:52Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13246","snapshot_observed_at":"2026-08-12T15:59:30.118621Z","title":"Towards federated learning on time-evolving heterogeneous data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.13740","last_updated":"2024-11-20T22:49:28Z","snapshot_observed_at":"2026-08-14T05:37:50.927736Z","submitted_at":"2024-11-20T22:49:28Z","title":"Federated Continual Learning for Edge-AI: A Comprehensive Survey","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T15:59:30.118621Z"},"links":{"cited_paper":"/paper/2112.13246","citing_paper":"/paper/2411.13740"},"observation_digest":"sha256:6b7cc2c7b6fe8618aef8d98391be2850f6c9a8f0758d33da1fa5c02ecfa3d9f1","observation_id":"fa8f2dd1-6cea-4a65-a32a-5720890dbb50","resolution":{"observed_at":"2026-08-12T15:59:30.118621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13246","last_updated":"2023-02-20T04:10:03Z","snapshot_observed_at":"2026-08-14T05:37:01.513042Z","submitted_at":"2021-12-25T14:58:52Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13246","snapshot_observed_at":"2026-08-11T12:46:50.678432Z","title":"Towards federated learning on time- evolving heterogeneous data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.13840","last_updated":"2025-05-06T08:55:42Z","snapshot_observed_at":"2026-08-14T03:09:29.918315Z","submitted_at":"2024-12-18T13:33:28Z","title":"Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey","version":2},"reference_index":176,"source":"pdf_text","source_observed_at":"2026-08-11T12:46:50.678432Z"},"links":{"cited_paper":"/paper/2112.13246","citing_paper":"/paper/2412.13840"},"observation_digest":"sha256:dc58c0e68692eea56b08b472091a962d6ce79b10ffc80b308c04d8de7ff875f2","observation_id":"9658bd22-79ae-4b06-948a-69d7c20a2c08","resolution":{"observed_at":"2026-08-11T12:46:50.678432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13246","last_updated":"2023-02-20T04:10:03Z","snapshot_observed_at":"2026-08-14T05:37:01.513042Z","submitted_at":"2021-12-25T14:58:52Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","version":3},"cited_work":{"arxiv_id":"2112.13246","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.13246","snapshot_observed_at":"2026-08-05T18:34:19.135460Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","venue":"cs.LG","work_id":"ad7275cc-8494-4231-821d-018f2211f090","year":2021},"citing_paper":{"arxiv_id":"2508.14539","last_updated":"2025-08-20T08:42:34Z","snapshot_observed_at":"2026-08-13T02:54:16.078361Z","submitted_at":"2025-08-20T08:42:34Z","title":"FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T18:34:14.079531Z"},"links":{"cited_paper":"/paper/2112.13246","citing_paper":"/paper/2508.14539"},"observation_digest":"sha256:a0300dc8dbb4bc6597387d2cc0d60a5f21a47b47ed39b7a58d2595e120036088","observation_id":"e1451c8f-06b6-4599-8e56-f7c59c864f43","resolution":{"observed_at":"2026-08-05T18:34:19.213610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2112.13246/citation-record","integrity":"/paper/2112.13246/integrity","json":"/paper/2112.13246/citation-record.json","paper":"/paper/2112.13246"},"outbound":[],"paper":{"arxiv_id":"2112.13246","last_updated":"2023-02-20T04:10:03Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T05:37:01.513042Z","submitted_at":"2021-12-25T14:58:52Z","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2112.13246."}