{"as_of":"2026-08-07T18:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d9296e51e105cfeb2a1eee88d4e05f9bcc1ea3a11fdedc51b1d3425edce80f6","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:38:05.089013Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.10430/citation-record","integrity":"/paper/2507.10430/integrity","json":"/paper/2507.10430/citation-record.json","paper":"/paper/2507.10430"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:38:06.449829Z","title":"Brendan McMahan, Brendan Avent, Aurélien Bellet, and Mehdi Bennis et al","venue":null,"work_id":"0c19ecc6-f424-44f2-afe2-0f8d741784d8","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.676055Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:cdcb26d7c04d1033a54a1ac5f93ec735cd98617c1ebf0ffd06ae06088b46ab36","observation_id":"410dfb99-ebe0-439d-b59d-2bab798ff253","resolution":{"observed_at":"2026-08-06T17:38:06.454307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.434502Z","title":"Trustworthy federated learning: Privacy, security, and beyond","venue":null,"work_id":"13321549-c977-4ee8-82cf-e855e40c65a1","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.682029Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:bbf254a4a39ee60a524c22ef2dde73580b0c345e1ab81599d810c7b8845d0743","observation_id":"3edd3e68-972c-4ea8-a1bc-25f721a1b5a8","resolution":{"observed_at":"2026-08-06T17:38:06.439721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.420042Z","title":"Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning","venue":null,"work_id":"4928a44f-9549-40db-b23a-f04921f43e57","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.687272Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:da8a37d7d8a0731ca4170b807a0761142e664db2125cbac336731d203b0ff992","observation_id":"c5455f36-937d-4625-902e-1ef3e69f83a3","resolution":{"observed_at":"2026-08-06T17:38:06.424426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.405890Z","title":"From distributed machine learning to federated learning: a survey","venue":null,"work_id":"5e6356ab-880b-4de2-b34d-3ed7a5e8ec1a","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.692551Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:7452c90ecd2516bbb2e97439da55b90211daad2725b5183a1fd7e4a73abebc91","observation_id":"2c76ad3b-08a1-43c0-8202-1ad7f66b114e","resolution":{"observed_at":"2026-08-06T17:38:06.410306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.391189Z","title":"General data protection regulation","venue":null,"work_id":"9e965517-21a4-4e97-aebd-0104ce5b7391","year":2016},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.699012Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:101d4d8ba56a9bf260077e066fc78f3bf6d119b4e5de32d1e28a7d717b17af01","observation_id":"b4a3e6eb-c4c2-4b60-b1e4-72ac262fb245","resolution":{"observed_at":"2026-08-06T17:38:06.395774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.374237Z","title":"California consumer privacy act home page","venue":null,"work_id":"035bcc68-7a82-4d97-94e2-d21645fbce10","year":2020},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.704817Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:22e20c7de18e8460570afb5fd7d0ff77318b9e215c3a32817fd7a98c1f0317a1","observation_id":"0f6a0598-5707-4b46-87d3-1adeeec0556c","resolution":{"observed_at":"2026-08-06T17:38:06.380064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.355897Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":"68b80628-2237-480d-a3d0-21a20594d57a","year":2017},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.711705Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:5f8d95f9f8a73d5becc44f775d1b106443eb36d1de355cf963d7aaeb31698a67","observation_id":"1117acd5-3bda-4d28-8eb8-347aa99ba5d3","resolution":{"observed_at":"2026-08-06T17:38:06.361693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.339468Z","title":"Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments","venue":null,"work_id":"ecadda0e-f5ab-44df-8c11-35011ef4d00c","year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.716883Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:e3ef8baa83967c16763f2c55598ba769d584c393c2f46a2c846b004cd6cf0776","observation_id":"f39f06d1-bee9-4f04-bf36-513d8df9dcb1","resolution":{"observed_at":"2026-08-06T17:38:06.344379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.322234Z","title":"Scaling distributed machine learning with the parameter server","venue":null,"work_id":"062bf5e9-f0e3-4419-9d3c-9d0fff75d4bb","year":2014},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.722601Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:6a616626c9f4d3a18c569f88da1b45c06d3a88ecb0b8262ba5c975ab95b4376c","observation_id":"a9228df8-b320-4653-9878-8023adb37441","resolution":{"observed_at":"2026-08-06T17:38:06.327795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.306379Z","title":"Vincent Poor","venue":null,"work_id":"13ad73be-5b59-4c5c-a339-db383a5f791f","year":2020},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.728147Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:7c519727c2c0948c1050e3a0c0f49809aa23d7102bf079b858fc9a2c4c2b515b","observation_id":"6c1c9c4f-b72a-4918-92bf-f27a7e21732f","resolution":{"observed_at":"2026-08-06T17:38:06.311301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.290091Z","title":"Multi-job intelligent scheduling with cross-device federated learning","venue":null,"work_id":"2f17ad68-f85b-40b5-877a-c4430a63588d","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.733469Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:8e9f9faba4f9dbffab559e99c83e54fbf973dd52a553e0543eba88829a376efb","observation_id":"ee761d31-6baa-4e82-8848-e9d0b4b5a173","resolution":{"observed_at":"2026-08-06T17:38:06.295181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.274247Z","title":"Efficient device scheduling with multi-job federated learning","venue":null,"work_id":"712a59bd-93c4-4ea9-bd21-ec7afae4b0c2","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.739079Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:2b90e91b178f8cd4bff2f49b731cfc99bc1b8daa0604f49c696fc5a70e3a9486","observation_id":"0d6c6193-3b57-45ef-952e-4a058250b352","resolution":{"observed_at":"2026-08-06T17:38:06.279260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.257922Z","title":"Federated learning on non-iid data silos: An experimental study","venue":null,"work_id":"0134ade5-f103-4bf3-a0e8-1d90bf482dab","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.745909Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:2045ef5f8a551e7a5f37f560fef15d94bb98457a007328f9d76dad8dacd5c65f","observation_id":"0a37ecb4-b26a-4b86-b08d-3e34ce7a3292","resolution":{"observed_at":"2026-08-06T17:38:06.263615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.241877Z","title":"On the convergence of fedavg on non-iid data","venue":null,"work_id":"28e2c253-1674-4ecb-a353-dd5ed01fb5c9","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.750619Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:5ea1750f15f5208ed1d3ce133d2eede36412d708fc6841897df510fd73ddb748","observation_id":"01ec500b-8a9a-44da-9adf-af2a5b772c13","resolution":{"observed_at":"2026-08-06T17:38:06.247046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.226183Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":"7f78b95e-9bf1-4932-ad13-e5d521b1ec4e","year":null},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.755387Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:e3a65e84acc57a31cc72e2295513bc7df8013bdbe72123b66ce54cd7c028f495","observation_id":"741bbe58-3729-45d6-b618-59cdd1ee30e6","resolution":{"observed_at":"2026-08-06T17:38:06.231280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.210748Z","title":"Jensen-shannon divergence and Hilbert space embedding","venue":null,"work_id":"8c3b6f60-1ad0-4ef9-b809-1439a349ca01","year":2004},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.760408Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:1b1b3686e96866ba749116ab711e85a395c50b6d88b4478ea9105c3a46ef1216","observation_id":"5e273bea-cd2b-4545-b7dc-7af11cd3394d","resolution":{"observed_at":"2026-08-06T17:38:06.215659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.194418Z","title":"Information theory and statistics","venue":null,"work_id":"4be2db38-3666-4f29-91d3-45dbfcb07937","year":1997},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.765018Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:bd2464efaab8e32514d47ded9c275b937ce78600476186eeda126675959419aa","observation_id":"384c9bc1-092a-436e-922d-27ec90a27c3b","resolution":{"observed_at":"2026-08-06T17:38:06.199499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.08647","last_updated":"2023-01-31T02:25:51Z","snapshot_observed_at":"2026-07-06T11:39:37.508345Z","submitted_at":"2021-08-19T12:20:31Z","title":"Multi-Center Federated Learning: Clients Clustering for Better Personalization","version":4},"cited_work":{"arxiv_id":"2108.08647","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.08647","snapshot_observed_at":"2026-08-06T17:38:05.253729Z","title":"Multi-Center Federated Learning: Clients Clustering for Better Personalization","venue":"cs.LG","work_id":"29cde1f2-4328-49ba-b54a-56742a22c398","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.769732Z"},"links":{"cited_paper":"/paper/2108.08647","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:7bdbdb24faf62564898d19ddc6993c78d14ce76323bc14eac62aff74ec99e632","observation_id":"73f3bdb2-d817-44be-b6ef-b5feb279f3ba","resolution":{"observed_at":"2026-08-06T17:38:05.260746Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.175816Z","title":"Towards federated learning at scale: System design","venue":null,"work_id":"883faba9-ab2b-4aab-adeb-fcf15c0fec08","year":null},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.775743Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:7bcbe5d49d4fe487bc30b339a7e887db3a5c3a45d4f059039a616b16e9cf6da1","observation_id":"95236e4f-8f13-4da2-827c-292543d2776d","resolution":{"observed_at":"2026-08-06T17:38:06.181267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.156980Z","title":"Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov","venue":null,"work_id":"5c0d3efc-6561-4296-a252-84758b875e63","year":1929},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.781694Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:9884654459a6643e487e1291f276812d5d8368ed1aceca3a4d811fc9c26a0800","observation_id":"8696325d-e313-4d24-8806-b764f8ba70bc","resolution":{"observed_at":"2026-08-06T17:38:06.163362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.139648Z","title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","venue":null,"work_id":"bb978b42-afab-43c0-86e0-4c39acd73eaf","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.787232Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:e90cfb98fb1aef05fe48288e6bb654a2088b43579b3952ea9f545919defe3af8","observation_id":"66602b4a-5385-40d3-9c50-acb289fc222e","resolution":{"observed_at":"2026-08-06T17:38:06.144986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.122380Z","title":"Adaptive federated dropout: Improving communication efficiency and generalization for federated learning","venue":null,"work_id":"8df8b168-b5bc-4745-81ed-533e6c32424c","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.792328Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:78299b00f796913fc7861b888cb41504fdb3ba77ef399401f96a729cb0c6aba2","observation_id":"929add3c-cd28-4298-9f7d-ff581e57cb3d","resolution":{"observed_at":"2026-08-06T17:38:06.128344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.104299Z","title":"Federated dropout–a simple approach for enabling federated learning on resource constrained devices","venue":null,"work_id":"30753c01-81b0-4b11-86fc-c8b8cfc02f33","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.798359Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:4f4c2a6af0adfe129a98aeefe6ca9c8938d7691fd90d7e6f5f84350882c3fdae","observation_id":"5e8a0054-a991-4dff-a0fa-2ea9210d159c","resolution":{"observed_at":"2026-08-06T17:38:06.110117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.087873Z","title":"Federated learning based on dynamic regularization","venue":null,"work_id":"59f4e622-cead-4686-b138-c9cc97cab37e","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.803730Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:64cd60d8c7bb3affe09b68989fc32f6580fdc5f0e64013a088e151e6428fd53b","observation_id":"be337498-8e05-41c2-a40e-5ca93212d6c4","resolution":{"observed_at":"2026-08-06T17:38:06.093287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.070085Z","title":"Model-contrastive federated learning","venue":null,"work_id":"8964ab4d-afa7-45a5-8f82-31938a970398","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.809276Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:4b6e7c0d9c850eda90dbbba50dd487ce7e0c6d541417c9ce8f6e8b5bc905a046","observation_id":"fe608f1d-3cc8-437e-935f-10860429bb8b","resolution":{"observed_at":"2026-08-06T17:38:06.075683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.053465Z","title":"SCAFFOLD: Stochastic controlled averaging for federated learning","venue":null,"work_id":"bbaad8bf-0c90-4b07-aafd-a6cabc1ca73d","year":2020},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.813957Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:49c0cdd5025a325d974dc3548b920dcb2281553ac6a2821c0c1ba82a6d3bc867","observation_id":"87c74c6e-979c-4375-afe1-bf3f0277a444","resolution":{"observed_at":"2026-08-06T17:38:06.059509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.033421Z","title":"Partialfed: Cross-domain personalized federated learning via partial initialization","venue":null,"work_id":"43fa4501-c9ad-486f-9adf-fc092ddfa83f","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.819331Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:16c6762b40adbff5d872929e1517e71e9d75a404d4df6c25115b2b8c21a3fe05","observation_id":"1afad173-ec60-49e9-9586-65f3448307d8","resolution":{"observed_at":"2026-08-06T17:38:06.039855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:06.014948Z","title":"Sageflow: Robust federated learning against both stragglers and adversaries","venue":null,"work_id":"fc55c82a-5437-4ef3-8e6e-810ba610d248","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.825877Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:791e62d8b4dbc574f67514136745615336d7b14865aee813b315037f1f89006d","observation_id":"e91240fa-6e03-4fe9-b7a6-eb70953f4987","resolution":{"observed_at":"2026-08-06T17:38:06.020608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.998456Z","title":"Efficient asynchronous federated learning with sparsification and quantization","venue":null,"work_id":"ad503d6e-aa4a-4b87-ad30-6eaf4e8ea201","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.830995Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:25e3dbbc9bd3a2b6363a794ce1e9b64fc42d32134a2e1d6dae3a099d7a0ab810","observation_id":"16a48242-ceb0-41b4-9516-73ecf4825834","resolution":{"observed_at":"2026-08-06T17:38:06.003474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.982031Z","title":"Aedfl: efficient asyn- chronous decentralized federated learning with heterogeneous devices","venue":null,"work_id":"47e44e91-a4f7-4e7b-a159-723dc34c9254","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.835913Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:37af65a4800ec1d7da8eaf89c995f873695443d8fb2e493fc8ceb9a46eaa0ded","observation_id":"81219de0-43d5-4ab9-951d-8c95ac207b7e","resolution":{"observed_at":"2026-08-06T17:38:05.987033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.962999Z","title":"Efficient federated learning with timely update dissemination","venue":null,"work_id":"92154457-c639-424e-900e-f34de7394e4b","year":2025},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.842064Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:dd87f342a22012eb52d1057243e71a51938130082807e465c24be8fe998fa7dc","observation_id":"591e6c4b-e932-4999-9d3b-65cf3cea1d2e","resolution":{"observed_at":"2026-08-06T17:38:05.970125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.944069Z","title":"Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update","venue":null,"work_id":"63f11a5d-aea3-4fec-a1d8-283b6b922d46","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.847096Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:a2a4d4479d0edbd259fc617c9d65cb095fd9f46a6b8fad07d0148de4ff5ff0e9","observation_id":"9d5e9706-8675-44e5-a72f-5a7c48fe7940","resolution":{"observed_at":"2026-08-06T17:38:05.950931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:04.852740Z","title":"Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.852740Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:c9029c6a77da7ce8cebd295a11cab0dfc7c3325aa013a25f0a40f70fa56ed818","observation_id":"676af114-be98-447a-b9ed-4e4ea58cb025","resolution":{"observed_at":"2026-08-06T17:38:04.852740Z","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":"2026-08-06T17:38:05.915089Z","title":"Exploring one-shot semi-supervised federated learning with pre-trained diffusion models","venue":null,"work_id":"1f33a563-48fc-4b49-b070-b156f51e7915","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.857867Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:2a817bc1ad6b2f00922c23d73231cccb8355fd7524a7f13a19d76153cc732eea","observation_id":"8d4e7da3-c66d-482c-ae00-e3e88eccc234","resolution":{"observed_at":"2026-08-06T17:38:05.920125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.899188Z","title":"Jamaloddin Golestani","venue":null,"work_id":"37b0f208-183c-41a1-9b49-56053e453403","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.864915Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:a5f8ab30454393d3c2d0438edae77e44ccfd3f9d545071233fb7ffd2e0c3b31d","observation_id":"4b278c30-0a30-4911-bb4f-62b771f4ff95","resolution":{"observed_at":"2026-08-06T17:38:05.903999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.882728Z","title":"Semi-cyclic stochastic gradient descent","venue":null,"work_id":"a74ef933-ac25-4757-aa8e-0463572ee554","year":2019},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.871857Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:b50b1b929cb76930a24c48f955513da0e8305f5844c9160ee49a34003481649d","observation_id":"02a4040e-b8a1-4dc9-bd72-09ffefbc724a","resolution":{"observed_at":"2026-08-06T17:38:05.888139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.17942","last_updated":"2023-03-31T10:13:01Z","snapshot_observed_at":"2026-07-06T15:10:26.549542Z","submitted_at":"2023-03-31T10:13:01Z","title":"Benchmarking FedAvg and FedCurv for Image Classification Tasks","version":1},"cited_work":{"arxiv_id":"2303.17942","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.17942","snapshot_observed_at":"2026-08-06T17:38:05.227577Z","title":"Benchmarking FedAvg and FedCurv for Image Classification Tasks","venue":"cs.LG","work_id":"6db2e371-4ee5-46ab-970c-43e79eb18464","year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.878844Z"},"links":{"cited_paper":"/paper/2303.17942","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:40c75fc8b354ce0584aa9f66a7c036661268c3982f1e3fa6343503c7ac3ffabe","observation_id":"3d9b4e57-2527-4e94-a517-d7ab6c4d601d","resolution":{"observed_at":"2026-08-06T17:38:05.234829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.863059Z","title":"Fair federated learning under domain skew with local consistency and domain diversity","venue":null,"work_id":"9a8c71ae-42bb-4712-89f0-220bc6f73909","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.884700Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:f7c1b98ddf7e08c976dcdd995cc5e046efb642c774010cb769a2e021cb95e0e1","observation_id":"a182ebca-cfc2-4ca8-ba99-3e89f2d38123","resolution":{"observed_at":"2026-08-06T17:38:05.869611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.845151Z","title":"Accelerated federated learning with decoupled adaptive optimization","venue":null,"work_id":"33ef7020-9ab8-4ca3-a642-c067432dabc5","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.890753Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:94990e4c2944cedc66eee1cb6ba596a10709fae976139d5e064ec4ba7a6d72cf","observation_id":"f1e0467a-989c-4d6a-bf78-361079113464","resolution":{"observed_at":"2026-08-06T17:38:05.850922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.829225Z","title":"Adaptive gradient-based meta-learning methods","venue":null,"work_id":"ad7f0043-4f63-4424-a958-e4850bed3a66","year":2019},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.896032Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:cf9dcf4ca64913018333c3cfba67e1967369bbffefe745445e62d18b366513bc","observation_id":"a3dad1d4-57ca-4af4-8f31-31a9b130a396","resolution":{"observed_at":"2026-08-06T17:38:05.834505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.812778Z","title":"Federated multi-task learning","venue":null,"work_id":"c2265d78-a0fd-487a-8ef2-134882c36a10","year":2017},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.901302Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:50d81b0eadc4d3471d129d2a96f7de174cb03a413fdfe77e54237156fe6ae3ee","observation_id":"17059868-9606-4ccc-9695-78c2d5662390","resolution":{"observed_at":"2026-08-06T17:38:05.818186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.796084Z","title":"Adversarial collaborative learning on non-iid features","venue":null,"work_id":"a7821121-5b38-4082-b51c-cf31ce5d4d73","year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.906680Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:0fd8cac437783f4358d76c1ff808d065427022a32e5af0a2a1c2531d512a3906","observation_id":"90036c44-590a-45e8-b891-90a6e74d81ca","resolution":{"observed_at":"2026-08-06T17:38:05.801133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.779957Z","title":"Generalizable heterogeneous federated cross-correlation and instance similarity learning","venue":null,"work_id":"6ae9e7e0-4172-4090-a68a-726436d3de70","year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.912901Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:a9f8a61c644645da07af3a9f28688a84159f3e13272592f2df6ebea491b3e161","observation_id":"60bb6750-a018-4dac-a89c-f836529df304","resolution":{"observed_at":"2026-08-06T17:38:05.784872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.763344Z","title":"Bayesian nonparametric federated learning of neural networks","venue":null,"work_id":"6b736c53-6a07-4d7a-8a1f-132a85fa7904","year":2019},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.919007Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:56b097bb7b63b0c3f0c0a2a469f49e7f7d3cd2c4030fe1403b4833d410d96b97","observation_id":"d5b01b30-fd4b-4658-a864-0f1fd7b68222","resolution":{"observed_at":"2026-08-06T17:38:05.769323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.743087Z","title":"Ensemble distillation for robust model fusion in federated learning","venue":null,"work_id":"1399d8d6-983f-435b-8f11-8f40a9ed32a4","year":2020},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.924179Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:7d43d9d0031d508c717c5f189bbfbae195982c375da90dee23e30df99762fa81","observation_id":"637bdf00-758c-4350-a5e9-d9125979b79a","resolution":{"observed_at":"2026-08-06T17:38:05.748512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.725971Z","title":"An upload-efficient scheme for transferring knowledge from a server-side pre-trained generator to clients in heterogeneous federated learning","venue":null,"work_id":"6dc0111a-6460-4784-bd64-accd8c1dab41","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.929958Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:31d92f72c1a4d0ec9183fbfb4ae64331d3459633f631dd65de1c3cbdfa2e62aa","observation_id":"110d5c90-545d-4281-9312-ae824f94aac5","resolution":{"observed_at":"2026-08-06T17:38:05.731874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02154","last_updated":"2025-03-04T00:37:33Z","snapshot_observed_at":"2026-08-07T17:31:58.898208Z","submitted_at":"2025-03-04T00:37:33Z","title":"AugFL: Augmenting Federated Learning with Pretrained Models","version":1},"cited_work":{"arxiv_id":"2503.02154","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.02154","snapshot_observed_at":"2026-08-06T17:38:05.199230Z","title":"AugFL: Augmenting Federated Learning with Pretrained Models","venue":"cs.LG","work_id":"879f7201-1a09-4bf3-b10a-f8a121c8154a","year":2025},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.934996Z"},"links":{"cited_paper":"/paper/2503.02154","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:98399e222ff571bcc2d8f5e5d3cd24d1b2c751b10c5d663721257f3d002592c5","observation_id":"9c991f12-511e-4f3c-8331-ec386250cbcf","resolution":{"observed_at":"2026-08-06T17:38:05.206865Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17431","last_updated":"2024-03-29T05:51:53Z","snapshot_observed_at":"2026-07-06T16:54:14.216770Z","submitted_at":"2023-11-29T08:21:42Z","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17431","snapshot_observed_at":"2026-08-06T17:38:04.941422Z","title":"Grounding foundation models through federated transfer learning: A general framework","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.941422Z"},"links":{"cited_paper":"/paper/2311.17431","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:6c5b1285e1e584ee9c97b4ddb6af308a0e7907a87873d3175449cd46810c3547","observation_id":"cde9f457-f3c6-4b9f-b01c-c983ed71890a","resolution":{"observed_at":"2026-08-06T17:38:04.941422Z","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":"2026-08-06T17:38:05.708795Z","title":"Big-fed: Bilevel optimization enhanced graph-aided federated learning","venue":null,"work_id":"74ea1810-78d0-49b6-8300-b6b2ac234add","year":null},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.947586Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:798ea4d9a35182ee9cd6436a0cbd07082187608748a6654461eed1e774f2c942","observation_id":"8c4364d0-2a31-47ad-83c6-3de55cecb7fc","resolution":{"observed_at":"2026-08-06T17:38:05.714062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.691143Z","title":"Model pruning enables efficient federated learning on edge devices","venue":null,"work_id":"7ba087f7-bb11-4934-bab8-37960b133945","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.953236Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:dc38c8cfff56cfb290e7a9cd78bbf2b10072921f96368101675aa0a930579908","observation_id":"19246e48-24d3-4001-83fc-bfed8d9fbe28","resolution":{"observed_at":"2026-08-06T17:38:05.697233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.673685Z","title":"Federated dynamic sparse training: Computing less, communicating less, yet learning better","venue":null,"work_id":"5e3d1f14-3b24-412f-9909-b0e831ed3532","year":null},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.958258Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:9e0a29890ddd36e1cd49377b56e9082900abd5c0e3bfc1d77bf7ba3a1f82160b","observation_id":"5ca28982-1148-4cdc-b83e-cd9552eab329","resolution":{"observed_at":"2026-08-06T17:38:05.679291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.656358Z","title":"Federated learning from pre-trained models: A contrastive learning approach","venue":null,"work_id":"be470f4d-8054-45ad-a6e6-86f0208160f7","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.963239Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:e5ea021aa2665553d007a22efc3a86d9e49d896e804736106b40f2f35afecff2","observation_id":"35df1fb6-5f85-4c60-8065-e11b0ca17cdc","resolution":{"observed_at":"2026-08-06T17:38:05.662139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.634467Z","title":"Oort: Efficient federated learning via guided participant selection","venue":null,"work_id":"81633093-b5da-4af4-8a30-eb706484524b","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.968229Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:d967d7f15682fef05a2ad0c25764fa439477d92a56ab6331ca4a7eeabe95d8f7","observation_id":"69dd4713-4adc-4ed1-9ad7-33b58212859f","resolution":{"observed_at":"2026-08-06T17:38:05.639501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.616831Z","title":"Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications","venue":null,"work_id":"9b1c9930-e4e6-4759-ae3a-d1a3e39d9200","year":2019},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.974572Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:8cb2da94c187aba74af560b2b35f038115862acf1b2e11e7aba62c2db7f316c7","observation_id":"7735abfd-7eb4-48ca-b97a-6fe201786576","resolution":{"observed_at":"2026-08-06T17:38:05.622374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.600798Z","title":"Client selection for federated learning with label noise","venue":null,"work_id":"c45a54d4-aaf5-4cb1-acb8-89bddca7e206","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.981363Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:ea41acd8be33cb80cac9466a6dc1ffcc8275bf5a710638b781d3169c5fc5f678","observation_id":"d0afdff0-5e1f-4cff-9702-96a48a3d2424","resolution":{"observed_at":"2026-08-06T17:38:05.605897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.583314Z","title":"FedDUAP: Federated learning with dynamic update and adaptive pruning using shared data on the server","venue":null,"work_id":"a415ff83-a59d-4de7-bb1a-835697e25957","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.987262Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:88fba138d7e065fcbacdac04d6d3c54a998c191bd80103f6572aa84d80ed664b","observation_id":"61950c60-792d-4deb-b942-dd9a33b673b2","resolution":{"observed_at":"2026-08-06T17:38:05.589182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.563800Z","title":"Efficient federated learning using dynamic update and adaptive pruning with momentum on shared server data","venue":null,"work_id":"ede7e5ab-3fc0-4684-a0bd-6a2af517317d","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.992286Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:645beddd15be61899f44130af720ed76fe191fca744a73b00c4c1153e269d2dd","observation_id":"b0e2d63a-95f8-4f82-9324-4bda7dd1f64a","resolution":{"observed_at":"2026-08-06T17:38:05.570011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.542087Z","title":"An improved federated learning algorithm for privacy-preserving in cybertwin-driven 6G system","venue":null,"work_id":"8017fef2-919d-4d91-bb8a-00a9789ca1e4","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:04.996921Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:0b4c80febe3f9af988ed3b98b6f30145db478bf440d02038e2ad35ef6a6914d1","observation_id":"6f0f59b9-94c9-4ad2-9e40-8f711926797f","resolution":{"observed_at":"2026-08-06T17:38:05.549677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.522689Z","title":"Parallelized stochastic gradient descent","venue":null,"work_id":"b79dacf2-c27f-45d0-9466-f4ac79be25ca","year":2010},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.004057Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:158f39f270cbaa9cb6e8ba164a5b196c0c78e87ec2181ab08f6d3e296beb9ea8","observation_id":"4fc4fa06-8882-48ca-919f-2c0efe23bc1e","resolution":{"observed_at":"2026-08-06T17:38:05.527819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02246","last_updated":"2018-02-06T22:10:14Z","snapshot_observed_at":"2026-07-06T06:22:08.261855Z","submitted_at":"2018-02-06T22:10:14Z","title":"Approximation Methods for Bilevel Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02246","snapshot_observed_at":"2026-08-06T17:38:05.009695Z","title":"Approximation methods for bilevel programming","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.009695Z"},"links":{"cited_paper":"/paper/1802.02246","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:edf12aa52c4a58b6540326687fe5122e55c5e05ae07e90690651403e15aaf087","observation_id":"b5b2cd3c-15a5-4aad-809f-b488b9017089","resolution":{"observed_at":"2026-08-06T17:38:05.009695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.05170","last_updated":"2022-06-08T05:49:52Z","snapshot_observed_at":"2026-08-06T01:11:27.164078Z","submitted_at":"2020-07-10T05:20:02Z","title":"A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.05170","snapshot_observed_at":"2026-08-06T17:38:05.015545Z","title":"A two-timescale framework for bilevel optimization: Complexity analysis and application to actor-critic","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.015545Z"},"links":{"cited_paper":"/paper/2007.05170","citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:fd8937cf6bb300ff995fff4c2860ec51c287630e6bc68a584b0f05b468ede878","observation_id":"39d625f4-4756-4251-90ca-851abc9cd392","resolution":{"observed_at":"2026-08-06T17:38:05.015545Z","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":"2026-08-06T17:38:05.505556Z","title":"Hrank: Filter pruning using high-rank feature map","venue":null,"work_id":"0953d95a-85fc-412a-9e77-154ce6d8db88","year":2020},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.021531Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:77e329840acd3cbf12260757e487d4d9c4942418d43d0d04d81ae28ae141574b","observation_id":"d581df08-2f7b-48fa-aed7-ab49f8e802b1","resolution":{"observed_at":"2026-08-06T17:38:05.511131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.487428Z","title":"Seizing critical learning periods in federated learning","venue":null,"work_id":"38eee900-16ca-4e8d-a6c9-91ab4f9c7c43","year":2022},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.026795Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:bb4e2054aa94c254a21d1a277b77d6dbf2a2b2d45f0fb9b1c67f851f24c95d48","observation_id":"3ff0fb95-2f16-4a8d-8c82-8ac699426d2b","resolution":{"observed_at":"2026-08-06T17:38:05.493744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.469891Z","title":"Validating the lottery ticket hypothesis with inertial manifold theory","venue":null,"work_id":"f381b3a1-3ebf-4b62-bd34-98388394818f","year":2021},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.032406Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:18bc35a2b171d54f6fb9a67fb5c69e79dd677453b212aa9db2d2d3e0af5ae1ce","observation_id":"6e0c449e-29f1-4942-b405-c602e67bb718","resolution":{"observed_at":"2026-08-06T17:38:05.475404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.450677Z","title":"Fedas: Bridging inconsistency in personalized federated learning","venue":null,"work_id":"8a1e3a63-762b-406a-bf3e-f87226b22ff3","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.038310Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:2f0b4b2fb8f481c3222094034f97f26d3c1f42c6d530be80b860926ab19e40a9","observation_id":"41a28c3c-daee-4d83-96d5-9774c3ed2949","resolution":{"observed_at":"2026-08-06T17:38:05.456566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.043627Z","title":"Learning multiple layers of features from tiny images, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.043627Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:3eb95d0184938bd6ec2ef68255a321777a6af1e7659008459db751bc7b1b3d33","observation_id":"66d9877b-dd4f-42ce-bfcf-50f5718f1e57","resolution":{"observed_at":"2026-08-06T17:38:05.043627Z","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":"2026-08-06T17:38:05.422511Z","title":"Reading digits in natural images with unsupervised feature learning","venue":null,"work_id":"7f7a9633-3414-4fb8-9068-2634233ad144","year":2011},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.049028Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:58f5bdf9741149d5a8295dacdc279f66175d156240fe0b9ca2b55263036cca7e","observation_id":"9e4be60d-922d-4db7-90d6-a916978fdc25","resolution":{"observed_at":"2026-08-06T17:38:05.428137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.405756Z","title":"Tiny imagenet visual recognition challenge","venue":null,"work_id":"31dbc18d-6301-44a1-9a6d-3e6730becebf","year":2015},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.054415Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:5a5e3487ddfd399e0bdd43ccd196efdcffa42abe9d6f082b57d3affee5c69ba4","observation_id":"0188b00a-2738-4fb3-9a03-ec2b02c845d9","resolution":{"observed_at":"2026-08-06T17:38:05.412123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.388831Z","title":"Handwritten digit recognition with a back-propagation network","venue":null,"work_id":"873aeb60-551b-4763-9478-2d3adbeabafe","year":1989},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.059576Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:8c6aa671bc40b1af47b36be9e08472dfce20eb641fe8a73176692366b5b33979","observation_id":"f8564d1a-8f35-4dcd-a971-d675a8fce607","resolution":{"observed_at":"2026-08-06T17:38:05.394696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.371202Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":"dcd437eb-5407-4f4a-853a-2ce7e286e02b","year":2015},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.064625Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:bd0ff55af278e867d69b6fcafb1a1888a8cd05f763ce1a908bd3596c60e286df","observation_id":"44fc9697-237e-4340-8e5b-126fb8df68c3","resolution":{"observed_at":"2026-08-06T17:38:05.377237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.352767Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"4c6245d1-2f65-4ad3-8dc1-e2f70c69aea5","year":2016},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.069555Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:31f394adb90e76c36122763e4f5f49bcd8f8ea52de118f1164da9eb1a726b709","observation_id":"a084d7a5-eb08-4d8f-91a7-8d24b1e5b2f4","resolution":{"observed_at":"2026-08-06T17:38:05.358026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.334351Z","title":"Fisher information- based efficient curriculum federated learning with large language models","venue":null,"work_id":"58bcbdf8-18a8-4fc4-82c6-c949e7f58dd9","year":2024},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.075038Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:f3bf7d9e057ab0a3acf0926b96fe8d9b8401807f6f9d5fab941ac9099252948a","observation_id":"cd5c797c-799e-4dfd-80e5-4d441e583f3e","resolution":{"observed_at":"2026-08-06T17:38:05.340371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.315175Z","title":"Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization","venue":null,"work_id":"6a3d3597-dc2e-4df3-9a44-1298c337d55c","year":2023},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.079833Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:653ef8953c2669dc483313d99bea2eb43006d5026c2dc656cb653b4b80a8e449","observation_id":"67114d49-8a2e-461c-987a-d07b1a5eea14","resolution":{"observed_at":"2026-08-06T17:38:05.321602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.295790Z","title":"Optimal distributed online prediction using mini-batches","venue":null,"work_id":"26065aef-204a-426d-b268-ae9b61626589","year":2012},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.084443Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:da4de90387870dce052aa5c7b0d834c104b18c3ebf508beee1f0519b2be55c70","observation_id":"84b2c3c0-0de2-4416-b505-d58ba09cd323","resolution":{"observed_at":"2026-08-06T17:38:05.302243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T17:38:05.273001Z","title":"On the importance of the pearson correlation coefficient in noise reduction","venue":null,"work_id":"be2c9389-a8c8-465c-8397-8d48d41d6b66","year":2008},"citing_paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T17:38:05.089013Z"},"links":{"citing_paper":"/paper/2507.10430"},"observation_digest":"sha256:dab5b365c0b31194890a3f382e71230698410b2d32aa66a61274757ec8d8d784","observation_id":"1f30ef1c-a787-42e9-9077-0c58d009c0e5","resolution":{"observed_at":"2026-08-06T17:38:05.278532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.10430","last_updated":"2025-07-15T02:55:39Z","latest_version":2,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-06T17:28:55.926890Z","submitted_at":"2025-07-14T16:19:00Z","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":3,"verified_fuzzy":67},"total_outbound_references":75},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2507.10430."}