{"as_of":"2026-08-08T21:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75f486306840137018a3c6a7f00367669e625ba00c145c03694f1d7fd32f1bd7","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T22:44:41.414002Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2509.07198/citation-record","integrity":"/paper/2509.07198/integrity","json":"/paper/2509.07198/citation-record.json","paper":"/paper/2509.07198"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:38.724369Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:38.724369Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:0740dfce0402c77957faf2eab48450e121e2fbb84c860089479be9319644ae1b","observation_id":"fdea3456-2ed6-40de-9e69-2b0998028652","resolution":{"observed_at":"2026-08-04T22:44:38.724369Z","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":"2026-08-04T22:44:38.794254Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:38.794254Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:8a4b6ffaa2f5c5e6b0e7b31d7637eb0d2cb21a8ae2e2aa7d35e3b546081bf34d","observation_id":"a0007316-5787-4233-b0db-76f6af8e4fc3","resolution":{"observed_at":"2026-08-04T22:44:38.794254Z","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":"2026-08-04T22:44:44.131700Z","title":"Trajectory-based air-writing recognition using deep neural network and depth sensor","venue":null,"work_id":"a478b4ce-a08c-4056-b764-0c57f3e801ad","year":2020},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:38.977972Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:0bfffda2bdbd80a046b06f359e9f6a21119f03ba53705b6b06530d372b2cf8c4","observation_id":"806c4808-f761-4bf9-af25-ca028b4a9475","resolution":{"observed_at":"2026-08-04T22:44:44.352026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.964211Z","title":"Evolutionary clustering via message passing","venue":null,"work_id":"0cb13ebd-7efd-4abb-92f7-fcab179bfa82","year":2019},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.067579Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:a6254a3d4b1b9c51f453b0cbf13223bf95d8ee051910a862733fb1feb6f8b501","observation_id":"65f75c27-d34d-4b78-852b-c08976f0b6e6","resolution":{"observed_at":"2026-08-04T22:44:44.017704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.861233Z","title":"Dynamic local regret for non-convex online forecasting","venue":null,"work_id":"24231637-7ef2-4939-a5a9-e84e4ed15475","year":2019},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.144380Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:e2c8096d08181f8a395135a4cacab1987b162cad0cea71e4934120320217c3a7","observation_id":"16090c43-1981-4ab7-95bc-9a1558f281e8","resolution":{"observed_at":"2026-08-04T22:44:43.909264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.721065Z","title":"Evolutionary clustering","venue":null,"work_id":"e57af79b-7aa9-4256-a470-2786a54c7d01","year":2006},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.206568Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:d7b7d9ee86df0e0dc80b10a03ad7c7e4f85ee3677161aed9578ee06690af0a7c","observation_id":"fbf90e28-4599-4c19-9853-789eb908bc98","resolution":{"observed_at":"2026-08-04T22:44:43.749225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:39.264461Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.264461Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:86d6cae7644e19907fdf5fae6572a645bed775724b04797c47367dc2496e91c5","observation_id":"d7c418f3-07b3-4c62-a61b-fe2f03519f98","resolution":{"observed_at":"2026-08-04T22:44:39.264461Z","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":"2026-08-04T22:44:43.610338Z","title":"Exploring simple siamese representation learning","venue":null,"work_id":"f00c93ce-549f-41cc-84f0-fb090e0fc967","year":2021},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.356543Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:2b2734a767a05ceb996308b7b8e7d90bc59bdfadcdcc606b00b4a71cba77c58a","observation_id":"6654c0da-55d6-4a60-b1da-76f17c6e4284","resolution":{"observed_at":"2026-08-04T22:44:43.660642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.482103Z","title":"Fed-qssl: A framework for personalized federated learning under bitwidth and data heterogeneity","venue":null,"work_id":"57ab3b30-9d1f-4025-a216-32c78b5435c2","year":2024},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.464462Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:3799045ca6d16653a50c9d8323f6f3ace436bc00e3d2a9a4a50a0bb5071c1cd3","observation_id":"617ec5f9-7ea2-44b3-a770-98cb346781f6","resolution":{"observed_at":"2026-08-04T22:44:43.541741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2003.13461","last_updated":"2020-11-06T04:07:31Z","snapshot_observed_at":"2026-08-02T16:42:32.296692Z","submitted_at":"2020-03-30T13:19:37Z","title":"Adaptive Personalized Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.13461","snapshot_observed_at":"2026-08-04T22:44:39.575328Z","title":"Adaptive personalized federated learning","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.575328Z"},"links":{"cited_paper":"/paper/2003.13461","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:df2f72ee088e6f745d483365091256d8e801292ad186cb1224b760adfe0aa6b4","observation_id":"a9a1eb86-6995-4ae8-93d1-48e0b5ca5129","resolution":{"observed_at":"2026-08-04T22:44:39.575328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08472","last_updated":"2024-05-06T04:00:17Z","snapshot_observed_at":"2026-08-06T22:05:52.494138Z","submitted_at":"2024-04-12T13:41:29Z","title":"TSLANet: Rethinking Transformers for Time Series Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08472","snapshot_observed_at":"2026-08-04T22:44:39.641708Z","title":"Tslanet: Rethinking transformers for time series representation learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.641708Z"},"links":{"cited_paper":"/paper/2404.08472","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:df1cb818ba53166daf8e4d5086cb8cc1828676b780dab437631280a442af90b6","observation_id":"7bdfdd6f-659f-4fd0-8c23-a63cc905ba69","resolution":{"observed_at":"2026-08-04T22:44:39.641708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.02199","last_updated":"2019-01-04T14:33:08Z","snapshot_observed_at":"2026-08-02T22:31:55.248338Z","submitted_at":"2018-06-06T14:11:30Z","title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series","version":7},"cited_work":{"arxiv_id":"1806.02199","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.02199","snapshot_observed_at":"2026-08-04T22:44:41.678469Z","title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series","venue":"cs.LG","work_id":"3981709f-40c9-4be9-a536-7a8fc5970658","year":2018},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.712326Z"},"links":{"cited_paper":"/paper/1806.02199","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:b82b48f3dd6d07331c81f471be06fa62d4441748f4c67ab79071466e5ace1ad7","observation_id":"a8f4e69a-bacb-4c47-b953-ce0377d73c0a","resolution":{"observed_at":"2026-08-04T22:44:41.779514Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2310.04486","last_updated":"2024-05-09T10:11:23Z","snapshot_observed_at":"2026-07-06T16:28:55.209047Z","submitted_at":"2023-10-06T15:45:28Z","title":"T-Rep: Representation Learning for Time Series using Time-Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04486","snapshot_observed_at":"2026-08-04T22:44:39.809244Z","title":"T-rep: Representation learning for time series using time-embeddings","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.809244Z"},"links":{"cited_paper":"/paper/2310.04486","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:5264353d8623cc758cb50387bf9eed56f918b664fcf7fa55a693fbfefcb88f67","observation_id":"adfeca4c-9989-47e4-9a39-10f575fac1e2","resolution":{"observed_at":"2026-08-04T22:44:39.809244Z","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":"2026-08-04T22:44:43.334303Z","title":"Unsupervised scalable representation learning for multivariate time series","venue":null,"work_id":"a71551ab-7c63-4543-acf6-f91b7b931b3c","year":2019},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.860360Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:27dcd3a0cfcd589987fc0947668818d58665c19d8fbe1fa9e47552edef8d8692","observation_id":"7f998ef6-898b-4b9c-8893-a1caba6c1ad6","resolution":{"observed_at":"2026-08-04T22:44:43.383532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.180313Z","title":"An efficient framework for clustered federated learning","venue":null,"work_id":"5be3dc34-7e56-4427-875f-c69d8fe065ea","year":2020},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:39.914995Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:16d8454002d8db5261ccce651df18115fdfa30db8dd9a9a6345e23c52bc486e2","observation_id":"3ffeec14-3f33-4a4d-822f-523d906bed33","resolution":{"observed_at":"2026-08-04T22:44:43.235106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:43.060576Z","title":"Long short-term memory","venue":null,"work_id":"e3e9c0dd-ca20-4a75-a7ee-1b13ea55d73c","year":1997},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.018203Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:a26685b17e2d6c1c4e565b290237f8432c31d12e02dd728b8e4855b0d0159e3d","observation_id":"a7547488-5651-420f-80ff-86047f7e87c3","resolution":{"observed_at":"2026-08-04T22:44:43.116532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:42.947526Z","title":"How to escape saddle points efficiently","venue":null,"work_id":"47374677-ccb9-4749-baa5-acb73690619e","year":2017},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.096475Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:de87461c1687db9b834e2d4b54a8e2a9d5160335f82ca8b3627b3d9f790a3bd3","observation_id":"21897879-1bda-4ca5-9fc2-c4db60874126","resolution":{"observed_at":"2026-08-04T22:44:42.973706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-04T04:31:27.172482Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-08-04T22:44:40.213589Z","title":"Time-llm: Time series forecasting by reprogramming large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.213589Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:0a9ce715295f25c4a20d2c5420bc7289554aa80165a0f4120458ba68c60c5ffa","observation_id":"ae573f56-d393-4a71-8047-6f9cace50194","resolution":{"observed_at":"2026-08-04T22:44:40.213589Z","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":"2026-08-04T22:44:42.839977Z","title":"Dynamic clustering in federated learning","venue":null,"work_id":"96503f19-ff51-4c39-ae3a-1a0977754243","year":2021},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.276566Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:68bd2a8aeb77098c356a6a75ededa9438d3b57d6913d2d09423cbe070e546cc2","observation_id":"2c72f1fd-1386-426e-95e2-91bd379368ed","resolution":{"observed_at":"2026-08-04T22:44:42.888136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:42.703598Z","title":"Recent development and applications of sumo-simulation of urban mobility","venue":null,"work_id":"d62aaa69-0842-42d7-b5c6-528e359ca1c0","year":2012},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.329267Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:dfab853b6a6be9f706bf3ab6d8da38aedda90f2d227315ded044cf0ed75faedc","observation_id":"d3be6f32-c405-4d1d-8767-4da252b55ef5","resolution":{"observed_at":"2026-08-04T22:44:42.769447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:40.407492Z","title":"Transfer learning promotes acquisition of individual bci skills","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.407492Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:9725d1e146d88c216dbb538e49312f19c6e5a765764d1411405a20f81ab7449e","observation_id":"d7731255-3cd6-4528-9e8d-1748679f9e72","resolution":{"observed_at":"2026-08-04T22:44:40.407492Z","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":"2026-08-04T22:44:42.580873Z","title":"Federated learning with soft clustering","venue":null,"work_id":"4f8fcffc-f60b-40c3-b5bd-3093ecb2fd4e","year":2021},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.490009Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:eaa432bb6a8b9f95f9228bfa0af37b665d46d95aeeb677aa0834eedb336bc452","observation_id":"7fe77ab5-1c26-4feb-b898-968f002db8e9","resolution":{"observed_at":"2026-08-04T22:44:42.647524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:42.440239Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":"f609148e-2869-4525-8843-aca7c9ee1acf","year":2020},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.554995Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:a9472d8690d847f5ad0bf4809ddc70473d38bc42aaab0bad37491940b3b0a29e","observation_id":"73304ca0-8af3-4573-b2f6-892adaeb84a0","resolution":{"observed_at":"2026-08-04T22:44:42.504928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:42.320486Z","title":"Ditto: Fair and robust federated learning through personalization","venue":null,"work_id":"90e0f252-18bf-4852-8ba0-ec38a70aaefc","year":2021},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.581005Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:4dc3360ed00edbe7ab603ce41f8fe2daa252e1afca77c0095ff6e4824cefb08e","observation_id":"81565354-3ee3-4446-b844-dfcd22c69aa3","resolution":{"observed_at":"2026-08-04T22:44:42.369416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-08-08T00:10:09.692416Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-04T22:44:40.660528Z","title":"Three approaches for personalization with applications to federated learning","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.660528Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:ab4b0fef8920f5a05d19a3d6f506ffe453116b2409788c9de96933c7dcce2af6","observation_id":"79e19cdf-18b4-4e19-9fc8-7d7aefc37eee","resolution":{"observed_at":"2026-08-04T22:44:40.660528Z","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":"2026-08-04T22:44:40.716158Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.716158Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:68256474d08f935da5e61f8498ca0bd9f7ae9745cac28b151db2df0ff10b1361","observation_id":"305f9de7-aafe-4e36-bc0c-1a72348ffb8f","resolution":{"observed_at":"2026-08-04T22:44:40.716158Z","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":"2026-08-04T22:44:42.173567Z","title":"A greedy agglomerative framework for clustered federated learning","venue":null,"work_id":"ff078968-1a98-45d2-9235-e149966b7445","year":2023},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.795139Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:57024b788343a37369988dae243324e27655792fafdf1a64482e68e37305a78c","observation_id":"b7adcd49-b5d3-4ec6-b4bb-bbabf5982abc","resolution":{"observed_at":"2026-08-04T22:44:42.249701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-07-31T22:45:35.561492Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-04T22:44:40.860154Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.860154Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:6af903322d2f5fb605a8465ffbf7bbc0c9b66c43b42135d99c90fbe61129ae24","observation_id":"f00d1d5d-ebc8-4ec5-ab71-d7321e256b9c","resolution":{"observed_at":"2026-08-04T22:44:40.860154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.10947","last_updated":"2023-02-28T16:54:02Z","snapshot_observed_at":"2026-07-06T14:07:58.832663Z","submitted_at":"2022-10-20T01:32:41Z","title":"Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.10947","snapshot_observed_at":"2026-08-04T22:44:40.914234Z","title":"Does learning from decentralized non-iid unlabeled data benefit from self supervision? arXiv preprint arXiv:2210.10947, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.914234Z"},"links":{"cited_paper":"/paper/2210.10947","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:1ac926db2de971a8278cc8f3f1043e1ddda65452404f1a349ce54705747b8699","observation_id":"ea48fac1-3c2a-48e3-a206-d6156477772e","resolution":{"observed_at":"2026-08-04T22:44:40.914234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02186","last_updated":"2023-04-12T02:34:03Z","snapshot_observed_at":"2026-07-06T13:59:54.436175Z","submitted_at":"2022-10-05T12:19:51Z","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02186","snapshot_observed_at":"2026-08-04T22:44:40.997273Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:40.997273Z"},"links":{"cited_paper":"/paper/2210.02186","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:38936a93380a4fa7db7ca57f493bd773a8104ddbe728154c1f9ec3bc89ca93b1","observation_id":"e6148928-1dbb-4b3f-98c3-490800e2f850","resolution":{"observed_at":"2026-08-04T22:44:40.997273Z","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":"2026-08-04T22:44:42.046622Z","title":"Adaptive evolutionary clustering","venue":null,"work_id":"ebfb0732-8370-41b3-ab82-3fa9d0a8d856","year":2014},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:41.065442Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:5384aa1ca4e3f59b0f1a61480b8bcab6aeb433adb636b2687d501a0e26237b6a","observation_id":"cd7140a3-53d4-4de1-9519-d98f447fcb46","resolution":{"observed_at":"2026-08-04T22:44:42.105599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:44:41.937046Z","title":"Metaclusterfl: Personalized federated learning on non-iid data with meta-learning and clustering","venue":null,"work_id":"832442ae-b9a7-4405-9e09-5280fe10a5c5","year":2024},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:41.239645Z"},"links":{"citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:0ad84ce8a0175dabed22a8edfc2cbe3ca6924c8763ccce04f978770a9726294c","observation_id":"0af5c459-f3c3-4ede-bd4d-fc5bec68d5b2","resolution":{"observed_at":"2026-08-04T22:44:42.013222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-07-06T06:42:35.645776Z","submitted_at":"2018-06-02T04:45:58Z","title":"Federated Learning with Non-IID Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00582","snapshot_observed_at":"2026-08-04T22:44:41.414002Z","title":"Federated learning with non-iid data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-04T22:44:41.414002Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2509.07198"},"observation_digest":"sha256:2388850515be3bc7ada38dc0b169533575f8aed26d46f2ad8e1e74d1a8a99154","observation_id":"af028fa0-1a67-4b20-beec-1693ad8ac16d","resolution":{"observed_at":"2026-08-04T22:44:41.414002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.07198","last_updated":"2025-09-08T20:24:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T19:40:01.544831Z","submitted_at":"2025-09-08T20:24:40Z","title":"Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":33},"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 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2509.07198."}