{"as_of":"2026-08-21T10:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6bc853ccf4dfc472b8902c33a14d8cff58cdf00be17f2fae1f33baf5c65fc4d0","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:46:21.725143Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2601.22274/citation-record","integrity":"/paper/2601.22274/integrity","json":"/paper/2601.22274/citation-record.json","paper":"/paper/2601.22274"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.04093","last_updated":"2021-05-10T03:48:55Z","snapshot_observed_at":"2026-08-20T12:38:52.163905Z","submitted_at":"2021-05-10T03:48:55Z","title":"Elastic Weight Consolidation (EWC): Nuts and Bolts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04093","snapshot_observed_at":"2026-08-15T15:46:21.630688Z","title":"Elastic weight consolidation (ewc): Nuts and bolts.arXiv preprint arXiv:2105.04093,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.630688Z"},"links":{"cited_paper":"/paper/2105.04093","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:690197ad82d45b0c3b1152caf01173d50911cf15fe0f6fabb13df7beed86fece","observation_id":"d9bfbc4c-fcca-4330-9bb5-3af5302c0313","resolution":{"observed_at":"2026-08-15T15:46:21.630688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15378","last_updated":"2023-03-27T16:52:17Z","snapshot_observed_at":"2026-08-20T12:38:52.998996Z","submitted_at":"2023-03-27T16:52:17Z","title":"CoDeC: Communication-Efficient Decentralized Continual Learning","version":1},"cited_work":{"arxiv_id":"2303.15378","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.15378","snapshot_observed_at":"2026-08-15T15:46:22.017002Z","title":"CoDeC: Communication-Efficient Decentralized Continual Learning","venue":"cs.LG","work_id":"833ef902-1298-4734-a180-e8361bce7e22","year":2023},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.641342Z"},"links":{"cited_paper":"/paper/2303.15378","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:45094d838c0e59408ef075b55bb0b32c6157489d23359fb5486b5bcd1d515b30","observation_id":"e366e71f-7836-42d2-9d6e-9cd5d9c271d9","resolution":{"observed_at":"2026-08-15T15:46:22.022080Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T15:46:21.661810Z","title":"Reading digits in natural images with unsupervised feature learning","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.661810Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:dc472bcf0805df59d681b2ec40fd7e7035e2fb63aa7a8c9aad979319069f967d","observation_id":"54925f22-116b-4f68-951f-6d915052bc8c","resolution":{"observed_at":"2026-08-15T15:46:21.661810Z","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":"2026-08-15T15:46:21.666507Z","title":"Better generative replay for continual federated learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.666507Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:bbf06500dbef1f2852e118aecb7d01db09a782b30924ca15b1cf1155eb2396c4","observation_id":"059c6a85-22eb-438e-9ba7-43b26d75c08d","resolution":{"observed_at":"2026-08-15T15:46:21.666507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00295","last_updated":"2021-09-08T23:37:17Z","snapshot_observed_at":"2026-08-13T06:06:10.844945Z","submitted_at":"2020-02-29T16:37:29Z","title":"Adaptive Federated Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.00295","snapshot_observed_at":"2026-08-15T15:46:21.671639Z","title":"Adaptive federated optimization.arXiv preprint arXiv:2003.00295,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.671639Z"},"links":{"cited_paper":"/paper/2003.00295","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:d4a153ece6f9824746f0c931ce5431e3fe0bcf0bdeda0daee0f3929657f50845","observation_id":"157f6c99-b045-410e-b194-da545d716ec9","resolution":{"observed_at":"2026-08-15T15:46:21.671639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13900","last_updated":"2025-04-01T17:09:48Z","snapshot_observed_at":"2026-08-16T13:50:30.454326Z","submitted_at":"2024-05-22T18:13:38Z","title":"Rehearsal-free Federated Domain-incremental Learning","version":2},"cited_work":{"arxiv_id":"2405.13900","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.13900","snapshot_observed_at":"2026-08-15T15:46:21.805736Z","title":"Rehearsal-free Federated Domain-incremental Learning","venue":"cs.LG","work_id":"2b27126f-840d-444f-8641-b8ed921198a9","year":2024},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.681524Z"},"links":{"cited_paper":"/paper/2405.13900","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:61da573d0897824d011ed205e2456b43ac1247812360fc14e41e1e2aa1bd5079","observation_id":"45e6af44-ff99-474b-a8f8-de7f41a4402d","resolution":{"observed_at":"2026-08-15T15:46:21.812781Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14205","last_updated":"2025-02-20T02:35:17Z","snapshot_observed_at":"2026-08-20T12:38:57.591609Z","submitted_at":"2025-02-20T02:35:17Z","title":"Accurate Forgetting for Heterogeneous Federated Continual Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14205","snapshot_observed_at":"2026-08-15T15:46:21.695913Z","title":"Accurate forgetting for heterogeneous federated con- tinual learning.arXiv preprint arXiv:2502.14205,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.695913Z"},"links":{"cited_paper":"/paper/2502.14205","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:4a5a2eef1b3d10b1a8eb4026fd350a98499bf11efa0876abf1908d7e9ac7a284","observation_id":"94261611-37af-4a8f-b9f5-98b9c711b5ae","resolution":{"observed_at":"2026-08-15T15:46:21.695913Z","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":"2026-08-15T15:46:22.077832Z","title":null,"venue":null,"work_id":"9d72402d-1aa2-49e2-bafb-0fe0b61a2a17","year":2021},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.720606Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:34a2cf6584dfabe265f1c8c5ec5b742094d46e5f6f154ebb5f840afc8718463c","observation_id":"23621060-4092-4fa6-8537-c752e8c76b93","resolution":{"observed_at":"2026-08-15T15:46:22.082494Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T15:46:22.063230Z","title":"CLIENT-VS. SERVER-SIDEPROXIMALTERMS","venue":null,"work_id":"aa43cd54-0e4d-4eff-91fd-3b70ad2bde78","year":2010},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.725143Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:38d5b1216f5598c0b1edfea821c2a2ae9da7abd3cc7c3f051ee7ea989b2a798c","observation_id":"3098b1fd-902c-450d-ba53-6a1bf08f37b5","resolution":{"observed_at":"2026-08-15T15:46:22.067904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.04197","last_updated":"2021-09-09T12:09:53Z","snapshot_observed_at":"2026-08-20T12:40:13.096842Z","submitted_at":"2021-09-09T12:09:53Z","title":"A distillation-based approach integrating continual learning and federated learning for pervasive services","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.04197","snapshot_observed_at":"2026-08-15T15:46:21.691185Z","title":"A distillation-based ap- proach integrating continual learning and federated learning for pervasive services.arXiv preprint arXiv:2109.04197,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.691185Z"},"links":{"cited_paper":"/paper/2109.04197","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:4222c637ee3bc68a9c0716adc8ad43aa93b409322f6a821ad33eba952be4bb4f","observation_id":"a1038bce-04a8-4705-bd47-0e6bff05cf57","resolution":{"observed_at":"2026-08-15T15:46:21.691185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.11203","last_updated":"2021-05-04T03:30:18Z","snapshot_observed_at":"2026-08-20T12:37:38.541644Z","submitted_at":"2021-01-27T04:38:27Z","title":"Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.11203","snapshot_observed_at":"2026-08-15T15:46:21.705115Z","title":"Achieving linear speedup with partial worker participa- tion in non-iid federated learning.arXiv preprint arXiv:2101.11203,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.705115Z"},"links":{"cited_paper":"/paper/2101.11203","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:36cc9f487cfa02438508d6311f47e646f5f582df149add79fcce31438f864c03","observation_id":"02db74b9-dad8-4c0e-8499-be0e3e3e7912","resolution":{"observed_at":"2026-08-15T15:46:21.705115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11341","last_updated":"2023-10-17T15:24:02Z","snapshot_observed_at":"2026-08-20T12:40:24.296127Z","submitted_at":"2023-10-17T15:24:02Z","title":"Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning","version":1},"cited_work":{"arxiv_id":"2310.11341","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.11341","snapshot_observed_at":"2026-08-15T15:46:21.996260Z","title":"Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning","venue":"cs.CV","work_id":"9efecdcb-2488-4c5a-bf1e-12e06b8d31c8","year":2023},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.646495Z"},"links":{"cited_paper":"/paper/2310.11341","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:0703b358f9392c002b91cf5859d3a971524838810bf6f3b5b7747781ea44138a","observation_id":"fb789b51-bbd3-459d-9667-add8b00d637a","resolution":{"observed_at":"2026-08-15T15:46:22.001425Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.00497","last_updated":"2023-07-17T19:16:24Z","snapshot_observed_at":"2026-08-20T14:55:33.364776Z","submitted_at":"2023-07-02T07:06:45Z","title":"Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.00497","snapshot_observed_at":"2026-08-15T15:46:21.636179Z","title":"A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.636179Z"},"links":{"cited_paper":"/paper/2307.00497","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:08a1333f8504ff3b3bf68910e2f9457ee779ab8b00da913c35ff9799af5f33a6","observation_id":"d867fad0-7869-4094-b316-e89d7d4bf3a6","resolution":{"observed_at":"2026-08-15T15:46:21.636179Z","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":"2026-08-15T15:46:21.651799Z","title":"On the convergence of continual federated learning using incrementally aggregated gradients.arXiv preprint arXiv:2411.07959,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.651799Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:f8adc28ec735f66d215d53b45d5c38412df82f963510e8f0a42670bb0d9e9d4e","observation_id":"eec38035-3dc3-4c75-8a98-21ba6352d3ea","resolution":{"observed_at":"2026-08-15T15:46:21.651799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02189","last_updated":"2020-06-25T06:45:52Z","snapshot_observed_at":"2026-08-14T16:08:56.121684Z","submitted_at":"2019-07-04T02:04:56Z","title":"On the Convergence of FedAvg on Non-IID Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02189","snapshot_observed_at":"2026-08-15T15:46:21.656648Z","title":"On the convergence of fedavg on non-iid data.arXiv preprint arXiv:1907.02189, 2019a","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.656648Z"},"links":{"cited_paper":"/paper/1907.02189","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:4a03b5587ab7a60d5d4592ed3a0376ba04ee19ec54ace522b12101fb15eec36d","observation_id":"50dbbe89-d93e-4d08-ba23-eca449baedb3","resolution":{"observed_at":"2026-08-15T15:46:21.656648Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-15T15:46:22.092076Z","title":"However, most FCL methods lack theoretical guarantees","venue":null,"work_id":"2ed613fc-bde1-4d71-b215-f3be3afea8be","year":2024},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.714524Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:6458e90d902818cb26a7cda70357e13ac0f1523ae30cd27fd953a538dafeb5f5","observation_id":"d404cc1d-3669-48d3-9df6-7ad0cd0b3ae0","resolution":{"observed_at":"2026-08-15T15:46:22.096786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T15:46:22.107441Z","title":null,"venue":null,"work_id":"464a65dd-dd08-485b-b771-19a395e95224","year":2022},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.709721Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:2b54f6e29e38876f0968601a3d39f6d43696570adad2d63067ae921c25ce6992","observation_id":"093d9b50-1b94-453e-8ec8-809eec982650","resolution":{"observed_at":"2026-08-15T15:46:22.111750Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T15:46:21.676342Z","title":"Local sgd converges fast and communicates little.arXiv preprint arXiv:1805.09767,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.676342Z"},"links":{"cited_paper":"/paper/1805.09767","citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:f1e4b5449ae0cc7c2e448599827829f960457f43eed77d0471c4f661a3bd8941","observation_id":"7ce6cbf7-4f72-42b1-b2e7-61b7eae8678f","resolution":{"observed_at":"2026-08-15T15:46:21.676342Z","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":"2026-08-15T15:46:22.136591Z","title":"Unbiased look at dataset bias","venue":null,"work_id":"b28afeb3-b2ed-4d18-bec5-359f1a7744f6","year":2011},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.686259Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:0e5ade4e4398237b475d58d4d2f5d5186f08f38ff03a43f46f104674da36d427","observation_id":"8962e591-f574-43b4-9b98-4f8edaf4fb0e","resolution":{"observed_at":"2026-08-15T15:46:22.141403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T15:46:22.121367Z","title":"A proximal stochastic gradient method with progressive variance reduc- tion.SIAM Journal on Optimization, 24(4):2057–2075,","venue":null,"work_id":"3cd217bd-c2ad-4ea2-ba33-420da6e3cbe7","year":null},"citing_paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T15:46:21.700509Z"},"links":{"citing_paper":"/paper/2601.22274"},"observation_digest":"sha256:7190191afa21ae13cf7d050f9a7e4930b88e48efc6bcb4e8a2b3563244c439a1","observation_id":"c2681ab9-6c67-46d3-8ba1-422a59269018","resolution":{"observed_at":"2026-08-15T15:46:22.125969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2601.22274","last_updated":"2026-05-28T17:13:40Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T19:53:07.053355Z","submitted_at":"2026-01-29T19:44:15Z","title":"Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":3,"verified_fuzzy":4},"total_outbound_references":20},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2601.22274."}