{"as_of":"2026-08-11T08:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed8d84d67cfdcd3b215f8d897e18cb129bf9242cabd39247c60a8d5b203d8b6b","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":70,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":70,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":70,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":70,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T05:52:19.134479Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":285,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"1909.06335","last_updated":"2019-09-13T17:26:20Z","snapshot_observed_at":"2026-08-02T11:40:53.964079Z","submitted_at":"2019-09-13T17:26:20Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-17T17:37:07.719640Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/1909.06335"},"observation_digest":"sha256:2b9c5482e18c9e6f6ba0e76674286c6774a3e22ac971ffd17a1433d6b888371a","observation_id":"fdfe475e-8ec5-41ef-b10a-96abefbb6fa0","resolution":{"observed_at":"2026-05-17T17:37:07.781054Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2003.00295","last_updated":"2021-09-08T23:37:17Z","snapshot_observed_at":"2026-07-06T09:01:12.515300Z","submitted_at":"2020-02-29T16:37:29Z","title":"Adaptive Federated Optimization","version":5},"reference_index":209,"source":"arxiv_source","source_observed_at":"2026-05-21T10:30:58.601351Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2003.00295"},"observation_digest":"sha256:a5d228fb708f3099aec052763a2aeaea9f4fe861f76a6cf0edd29b7cccd2d5c2","observation_id":"5ccf8794-e7a2-4001-9460-68871a76d545","resolution":{"observed_at":"2026-05-21T10:30:58.785016Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2305.16272","last_updated":"2026-04-13T14:48:26Z","snapshot_observed_at":"2026-07-06T15:33:31.386787Z","submitted_at":"2023-05-25T17:28:41Z","title":"Incentivizing Honesty among Competitors in Collaborative Learning and Optimization","version":5},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-24T08:48:51.347093Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2305.16272"},"observation_digest":"sha256:2fedcc885148f5d8793a7928f1ac070dd0696b88023f5e685befa2c0c86ce0cf","observation_id":"c0c53394-7c45-4a46-98af-6982c07ed6a1","resolution":{"observed_at":"2026-05-24T08:49:13.860206Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-11T05:52:19.134479Z","title":"Leaf: A benchmark for federated settings,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.17081","last_updated":"2024-12-22T16:13:00Z","snapshot_observed_at":"2026-08-11T05:46:25.112937Z","submitted_at":"2024-12-22T16:13:00Z","title":"Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T05:52:19.134479Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2412.17081"},"observation_digest":"sha256:6afc2f4384d520cdb723f5dd3ce97a484a2b071796702008865403883e7db9fc","observation_id":"9b6dbe24-251c-4c71-888d-d10602544d6f","resolution":{"observed_at":"2026-08-11T05:52:19.134479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-11T04:25:52.423409Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.18904","last_updated":"2024-12-25T13:35:54Z","snapshot_observed_at":"2026-08-11T04:18:45.768435Z","submitted_at":"2024-12-25T13:35:54Z","title":"FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T04:25:52.423409Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2412.18904"},"observation_digest":"sha256:3085ce1f6e1912e0a3f00a00b5359bd3a904a725096cb644bdbc9f2c2ba6d279","observation_id":"74adc55f-7bd5-4fbf-a450-fc40ead6d48b","resolution":{"observed_at":"2026-08-11T04:25:52.423409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-10T21:11:35.334514Z","title":"Brendan McMahan, Virginia Smith, and Ameet Talwalkar","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.06062","last_updated":"2025-01-10T15:46:19Z","snapshot_observed_at":"2026-08-10T21:03:47.825807Z","submitted_at":"2025-01-10T15:46:19Z","title":"Personalized Language Model Learning on Text Data Without User Identifiers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:35.334514Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2501.06062"},"observation_digest":"sha256:4e4f5e64daed2e09d01a53c9e2f4ded38dc319aa40a5602671097ebcc47346e4","observation_id":"f5bf19d9-5a0f-4a73-8a18-fc66a9e894ba","resolution":{"observed_at":"2026-08-10T21:11:35.334514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-10T18:58:03.702888Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.10877","last_updated":"2025-01-18T20:59:07Z","snapshot_observed_at":"2026-08-10T18:50:18.867929Z","submitted_at":"2025-01-18T20:59:07Z","title":"Distributed Quasi-Newton Method for Fair and Fast Federated Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T18:58:03.702888Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2501.10877"},"observation_digest":"sha256:a77f7eb01d34c83c6203b73045f643a294ad76b2eec81cf63bfea87f2ad44ec1","observation_id":"2be24f9d-1162-4318-a82c-410994089eb3","resolution":{"observed_at":"2026-08-10T18:58:03.702888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-10T14:02:44.135286Z","title":"Leaf: A benchmark for Federated Settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.16397","last_updated":"2025-01-27T03:29:02Z","snapshot_observed_at":"2026-08-10T13:56:11.005225Z","submitted_at":"2025-01-27T03:29:02Z","title":"THOR: A Generic Energy Estimation Approach for On-Device Training","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T14:02:44.135286Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2501.16397"},"observation_digest":"sha256:bc7ff7871dd2de66ee4278efd4b379ced7d5eaf38334cb691d687bfdfc4dd67b","observation_id":"31026867-41fe-484a-ac3d-b5f54ff7d1f4","resolution":{"observed_at":"2026-08-10T14:02:44.135286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-09T05:10:21.985127Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03340","last_updated":"2025-05-01T18:15:00Z","snapshot_observed_at":"2026-08-10T13:40:15.499184Z","submitted_at":"2025-02-05T16:33:36Z","title":"Interaction-Aware Gaussian Weighting for Clustered Federated Learning","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T05:10:21.985127Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.03340"},"observation_digest":"sha256:5b04b0fb2a196f27ab317130609a27a47ef685689e88d6716ab1f589fc942f0a","observation_id":"f7bdfed2-e5ba-4360-bc90-9bb073363463","resolution":{"observed_at":"2026-08-09T05:10:21.985127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-08T21:19:39.188001Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04850","last_updated":"2025-07-13T02:51:38Z","snapshot_observed_at":"2026-08-11T02:21:37.596678Z","submitted_at":"2025-02-07T11:39:27Z","title":"Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T21:19:39.188001Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.04850"},"observation_digest":"sha256:bf93e74d964ba599de355079a66a0d9b4825b57f1eb89fbbec772dcc03682c85","observation_id":"852adbf4-007b-4701-9c5b-bef5d489a50c","resolution":{"observed_at":"2026-08-08T21:19:39.188001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-08T21:10:23.164727Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04890","last_updated":"2025-02-14T12:36:02Z","snapshot_observed_at":"2026-08-10T00:01:19.958690Z","submitted_at":"2025-02-07T12:56:39Z","title":"Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T21:10:23.164727Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.04890"},"observation_digest":"sha256:17e1e4c7fc213167b9117a0c34817e971b9fa1a3cb72a25cf40b02434a4e378b","observation_id":"3ddb3446-6c30-447a-bdba-0aaf90ad41dc","resolution":{"observed_at":"2026-08-08T21:10:23.164727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-09T14:12:23.075049Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05211","last_updated":"2025-02-03T23:14:02Z","snapshot_observed_at":"2026-08-09T18:43:04.625886Z","submitted_at":"2025-02-03T23:14:02Z","title":"Decoding FL Defenses: Systemization, Pitfalls, and Remedies","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T14:12:23.075049Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.05211"},"observation_digest":"sha256:ac853147af53e633c0733b381ed943a29200ccabdbed3a01fadcabbd632bdae0","observation_id":"dd7f2a67-c436-439b-accb-5a50f969f4a5","resolution":{"observed_at":"2026-08-09T14:12:23.075049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T23:36:53.666170Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08829","last_updated":"2026-07-19T01:09:13Z","snapshot_observed_at":"2026-08-07T23:30:50.142137Z","submitted_at":"2025-02-12T22:35:29Z","title":"PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T23:36:53.666170Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2502.08829"},"observation_digest":"sha256:f39add69657394652d6ba2b175ab5443d7928ae14b3323e032985c2805f72115","observation_id":"759940c4-f894-4115-ad75-8b6156a169b9","resolution":{"observed_at":"2026-08-07T23:36:53.666170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2505.06907","last_updated":"2026-05-18T08:23:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-11T08:57:53Z","title":"A Survey on Foundation Models for Personalized Federated Intelligence","version":2},"reference_index":141,"source":"pdf_text","source_observed_at":"2026-05-22T15:32:15.293888Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2505.06907"},"observation_digest":"sha256:06e072977a1beba732d13497d70317b32c07fb4160f9c16e958993875889cb5c","observation_id":"b365c047-ae33-4148-9c83-36d740f9b0f0","resolution":{"observed_at":"2026-05-22T15:34:57.592497Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T15:21:58.242251Z","title":"Caruana, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-11T02:18:53.656529Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.242251Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:82c66c7fda0067108f1fd7fe558e42a246b575ff85e74bd208f28416f2cccdc0","observation_id":"40a6c29a-a711-4c79-ad40-0a60a0cb95bf","resolution":{"observed_at":"2026-08-07T15:21:58.242251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T14:11:28.690915Z","title":"Leaf: A benchmark for federated settings.arXiv preprint arXiv:1812.01097, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.19888","last_updated":"2026-03-14T13:54:14Z","snapshot_observed_at":"2026-08-07T14:02:04.233296Z","submitted_at":"2025-05-26T12:18:24Z","title":"Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:28.690915Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2505.19888"},"observation_digest":"sha256:50be774186d4489f586d45b3f34277dbcc1b0edfa42a4df7664a7e805cbe1412","observation_id":"9be32b7b-4711-48a0-913c-e44080891a44","resolution":{"observed_at":"2026-08-07T14:11:28.690915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T12:45:33.167489Z","title":"Leaf: A benchmark for federated settings,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.23588","last_updated":"2025-05-29T16:00:34Z","snapshot_observed_at":"2026-08-09T02:21:13.828072Z","submitted_at":"2025-05-29T16:00:34Z","title":"Accelerated Training of Federated Learning via Second-Order Methods","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:33.167489Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2505.23588"},"observation_digest":"sha256:41e65cc5e8709b7fc7d23372c831d984402ef7a025fff867639958d9588e38bb","observation_id":"1ef97720-65ed-4097-b68a-4acfc473b56f","resolution":{"observed_at":"2026-08-07T12:45:33.167489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T11:41:08.736791Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.01777","last_updated":"2025-06-02T15:20:54Z","snapshot_observed_at":"2026-08-07T11:31:29.400251Z","submitted_at":"2025-06-02T15:20:54Z","title":"DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:41:08.736791Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.01777"},"observation_digest":"sha256:f0b3aabb970ccc509c1fa1ad80a491c7125eb25ad2744fa4c04405de05903130","observation_id":"4065d6df-b067-4a01-9268-f457595566b8","resolution":{"observed_at":"2026-08-07T11:41:08.736791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T11:19:02.797411Z","title":"Leaf: A benchmark for federated settings.arXiv preprint arXiv:1812.01097,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02897","last_updated":"2026-07-23T07:22:59Z","snapshot_observed_at":"2026-08-09T03:16:50.174725Z","submitted_at":"2025-06-03T14:04:31Z","title":"Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T11:19:02.797411Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.02897"},"observation_digest":"sha256:0b546fa8dca78a2922fdd2cda19f6c491db774ba6a0459c2de09c6f2ac3efc04","observation_id":"ca160a99-dcf7-4d15-8d7d-0a6cf923b8d1","resolution":{"observed_at":"2026-08-07T11:19:02.797411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T10:55:14.880870Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.03954","last_updated":"2025-06-04T13:44:00Z","snapshot_observed_at":"2026-08-10T05:12:33.461373Z","submitted_at":"2025-06-04T13:44:00Z","title":"HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:55:14.880870Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.03954"},"observation_digest":"sha256:637d51852d92ac24cd165d77c41bf379470afe9b077999793490784a7d1dbe6a","observation_id":"28336cf0-d820-499d-b2d0-db1168e0ca81","resolution":{"observed_at":"2026-08-07T10:55:14.880870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T10:46:06.445477Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.04531","last_updated":"2025-06-05T00:48:55Z","snapshot_observed_at":"2026-08-09T03:17:02.325152Z","submitted_at":"2025-06-05T00:48:55Z","title":"HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T10:46:06.445477Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.04531"},"observation_digest":"sha256:30e27807481087b0b9f9c6fb07a379cdd5544c10c76c0ec2eaae4fa25ce1418a","observation_id":"68dc5e3c-4d4b-48ce-9f99-9ee51f8cd146","resolution":{"observed_at":"2026-08-07T10:46:06.445477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T10:43:52.882830Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.04672","last_updated":"2025-06-05T06:38:29Z","snapshot_observed_at":"2026-08-09T12:51:37.584399Z","submitted_at":"2025-06-05T06:38:29Z","title":"FedAPM: Federated Learning via ADMM with Partial Model Personalization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:43:52.882830Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.04672"},"observation_digest":"sha256:d8ae3f2ae216a6921d9907c4ca1df8ec2a8f0a835e32407e37f593bfdd9c1ec3","observation_id":"a057d532-3703-453c-978b-cbf1b12365e6","resolution":{"observed_at":"2026-08-07T10:43:52.882830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T10:34:46.596166Z","title":"LEAF: A benchmark for federated settings,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.04978","last_updated":"2025-06-05T12:49:22Z","snapshot_observed_at":"2026-08-08T16:44:38.608473Z","submitted_at":"2025-06-05T12:49:22Z","title":"Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:34:46.596166Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.04978"},"observation_digest":"sha256:b39aa41038778f7959fe42b65a1589c6a685a632bab80a0b47b338add0c6609d","observation_id":"6981c97d-5a2a-4b81-8d9f-22d1ff339cc2","resolution":{"observed_at":"2026-08-07T10:34:46.596166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-06T22:59:37.119915Z","title":"Leaf: A benchmark for federated set- tings.arXiv preprint arXiv:1812.01097, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.20245","last_updated":"2025-06-25T08:42:10Z","snapshot_observed_at":"2026-08-09T10:05:52.730495Z","submitted_at":"2025-06-25T08:42:10Z","title":"FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:59:37.119915Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.20245"},"observation_digest":"sha256:3b08a005c46d51a912abe732b3cb985112450f069c4fad2426a37c1f966f18b6","observation_id":"abb31d6d-edd0-487a-af44-dbb1119292db","resolution":{"observed_at":"2026-08-06T22:59:37.119915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2506.21095","last_updated":"2026-05-09T09:53:47Z","snapshot_observed_at":"2026-08-06T22:30:02.842734Z","submitted_at":"2025-06-26T08:43:12Z","title":"FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation","version":5},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T08:07:52.498347Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2506.21095"},"observation_digest":"sha256:5960d0d73647cdb0517ae97fa20341e8a2aec744830e690670fb018cd50cf2e2","observation_id":"c09b88ee-4134-48ba-ae87-6b7bc84058fb","resolution":{"observed_at":"2026-05-19T08:12:10.712066Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-06T20:37:13.157611Z","title":"Caldas, S","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.02295","last_updated":"2025-07-03T04:07:40Z","snapshot_observed_at":"2026-08-10T05:06:19.938072Z","submitted_at":"2025-07-03T04:07:40Z","title":"Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:37:13.157611Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2507.02295"},"observation_digest":"sha256:0cdb30dccb6de95fcaa2e2ea9e3f1fcdb130556727df3ec18265f740e65484cb","observation_id":"63a9f4a9-81a2-4f87-8e22-e0d3542fc6ba","resolution":{"observed_at":"2026-08-06T20:37:13.157611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-06T15:31:31.797217Z","title":"LEAF: A benchmark for federated settings,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.15816","last_updated":"2025-07-21T17:21:16Z","snapshot_observed_at":"2026-08-10T01:13:15.788556Z","submitted_at":"2025-07-21T17:21:16Z","title":"Federated Split Learning with Improved Communication and Storage Efficiency","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T15:31:31.797217Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2507.15816"},"observation_digest":"sha256:c32b85fa919162397d0e669faee43be90e88b107c61d8686420c95f503b0f862","observation_id":"4eb03127-bd08-4be2-9db7-884ab67dcad5","resolution":{"observed_at":"2026-08-06T15:31:31.797217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-06T15:06:48.385841Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.16731","last_updated":"2025-07-22T16:13:43Z","snapshot_observed_at":"2026-08-07T22:15:08.576638Z","submitted_at":"2025-07-22T16:13:43Z","title":"Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges","version":1},"reference_index":145,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:48.385841Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2507.16731"},"observation_digest":"sha256:28b15081a960384974e68c5c3aea23d2392c71a12525e29106fd417113fcaa26","observation_id":"4fc4a396-cf06-4e4a-848c-1db793780e4b","resolution":{"observed_at":"2026-08-06T15:06:48.385841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T23:09:44.794914Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.06183","last_updated":"2025-08-08T09:56:47Z","snapshot_observed_at":"2026-08-06T00:25:36.627605Z","submitted_at":"2025-08-08T09:56:47Z","title":"Differentially Private Federated Clustering with Random Rebalancing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T23:09:44.794914Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2508.06183"},"observation_digest":"sha256:0dca1ace339d24efb19b58d1e2b2e9c6b612d47125c5023deac623c007611572","observation_id":"b5c97601-afd5-461a-a710-9d4926db74f7","resolution":{"observed_at":"2026-08-05T23:09:44.794914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T18:34:13.355653Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.14539","last_updated":"2025-08-20T08:42:34Z","snapshot_observed_at":"2026-08-08T05:39:38.071915Z","submitted_at":"2025-08-20T08:42:34Z","title":"FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T18:34:13.355653Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2508.14539"},"observation_digest":"sha256:6e6befd1837ae5f9c21ee6c9b6f5c5f6a855439f8c75dff454e370668665038f","observation_id":"1def8fc6-14b0-4eb2-afcf-436d1c3cb05f","resolution":{"observed_at":"2026-08-05T18:34:13.355653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T16:20:23.380270Z","title":"Leaf: A benchmark for federated settings,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18737","last_updated":"2025-08-26T07:09:15Z","snapshot_observed_at":"2026-08-10T01:24:25.718080Z","submitted_at":"2025-08-26T07:09:15Z","title":"FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T16:20:23.380270Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2508.18737"},"observation_digest":"sha256:131d22a34ae97d1222496f8382a3ae3eb41daf88f447837612b37d26bd02a9e6","observation_id":"4cbee77e-8b8c-4a68-b685-1ab839bde23c","resolution":{"observed_at":"2026-08-05T16:20:23.380270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T05:02:47.794905Z","title":"Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.05833","last_updated":"2025-09-06T21:06:50Z","snapshot_observed_at":"2026-08-10T19:36:28.569856Z","submitted_at":"2025-09-06T21:06:50Z","title":"Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-05T05:02:47.794905Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2509.05833"},"observation_digest":"sha256:0a5b4c554da74e58e01ee7f03516a2090b24288522769c3bc62fb672d0e02c36","observation_id":"f6cd5155-daf5-4573-840d-d926e073039f","resolution":{"observed_at":"2026-08-05T05:02:47.794905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-04T21:06:25.989416Z","title":"Caldas, P","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08233","last_updated":"2025-09-10T02:19:56Z","snapshot_observed_at":"2026-08-06T06:55:03.018292Z","submitted_at":"2025-09-10T02:19:56Z","title":"Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T21:06:25.989416Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2509.08233"},"observation_digest":"sha256:3a54192833e859d8e2b21ff4c587021b970e4222b25429d9bc303eb3d30e749d","observation_id":"0fca57a2-aa20-4bf5-907f-e7e940bd904b","resolution":{"observed_at":"2026-08-04T21:06:25.989416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T10:32:25.438219Z","title":"Leaf: A benchmark for federated settings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08750","last_updated":"2025-09-04T08:19:56Z","snapshot_observed_at":"2026-08-11T02:55:25.781057Z","submitted_at":"2025-09-04T08:19:56Z","title":"PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:32:25.438219Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2509.08750"},"observation_digest":"sha256:7e20042617229253b841151d48ecf1eae786d980352ff1490aa53c91e7d54ef9","observation_id":"1dad18f0-8a11-4811-86c2-a62066b799dd","resolution":{"observed_at":"2026-08-05T10:32:25.438219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2510.07922","last_updated":"2026-05-01T03:08:45Z","snapshot_observed_at":"2026-07-06T22:32:07.945004Z","submitted_at":"2025-10-09T08:16:32Z","title":"SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T08:55:14.289595Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2510.07922"},"observation_digest":"sha256:b8c3c0c5ae63a1c0f89f3cf7e9f95a8469978801f7056edaf473bc4a5bb51310","observation_id":"8601a4d3-9e33-4e36-8064-df45141ee22b","resolution":{"observed_at":"2026-05-18T08:56:08.499714Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-04T09:30:11.167794Z","title":"Ken Chang, Niranjan Balachandar, Carson Lam, Darvin Yi, James Brown, Andrew Beers, Bruce Rosen, Daniel Rubin, and Jayashree Kalpathy-Cramer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.15300","last_updated":"2026-03-02T07:22:42Z","snapshot_observed_at":"2026-08-09T03:42:57.586327Z","submitted_at":"2025-10-17T04:17:00Z","title":"DFCA: Decentralized Federated Clustering Algorithm","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T09:30:11.167794Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2510.15300"},"observation_digest":"sha256:3995456f026dddf4cc95d568c907ce1596b823d445feb488d2b5d559ef4f4a01","observation_id":"f0e81c96-fbc8-4fd8-bb08-d4979cd96197","resolution":{"observed_at":"2026-08-04T09:30:11.167794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2512.05372","last_updated":"2026-08-09T12:12:02Z","snapshot_observed_at":"2026-08-11T08:14:02.450577Z","submitted_at":"2025-12-05T02:13:23Z","title":"Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-17T01:52:58.621227Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2512.05372"},"observation_digest":"sha256:a258b6015b16210bcbd5724249a4f4a61e5a9d13ffbfb57e41a6016c9853ebad","observation_id":"2e93ee92-84d0-44fc-815d-62573e287bdd","resolution":{"observed_at":"2026-05-17T01:53:51.047747Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-02T21:51:43.893539Z","title":"Leaf: A benchmark for federated settings.arXiv preprint arXiv:1812.01097, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.18900","last_updated":"2026-06-24T23:50:52Z","snapshot_observed_at":"2026-08-08T03:35:41.653638Z","submitted_at":"2026-02-21T16:45:56Z","title":"PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T21:51:43.893539Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2602.18900"},"observation_digest":"sha256:3d9a72a91edce96dcc35b7ed74ba9012963b151efc70d26cd1690a97cbd64d85","observation_id":"2e68e94f-ae3d-4b8f-82d4-d3249a434166","resolution":{"observed_at":"2026-08-02T21:51:43.893539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.10849","last_updated":"2026-04-12T22:48:51Z","snapshot_observed_at":"2026-07-06T22:59:23.126963Z","submitted_at":"2026-04-12T22:48:51Z","title":"Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:18:01.426404Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.10849"},"observation_digest":"sha256:cdf8085c856a73b865f19826e68e04808f8639e041a0ebf2a884dbbb17cfb35f","observation_id":"e2273c09-2183-4f40-8405-8cda05a6a88a","resolution":{"observed_at":"2026-05-11T10:51:03.028148Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.12348","last_updated":"2026-04-14T06:33:58Z","snapshot_observed_at":"2026-07-06T23:00:32.607699Z","submitted_at":"2026-04-14T06:33:58Z","title":"PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T15:54:44.593836Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.12348"},"observation_digest":"sha256:eb94a16bd98cddeec36d054513d40215c450ed8344bfe160d206934d6f26e4cd","observation_id":"38b3bb16-e0e0-44ef-a80f-69d66a703cbd","resolution":{"observed_at":"2026-05-11T09:36:14.867216Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.20161","last_updated":"2026-04-22T04:00:31Z","snapshot_observed_at":"2026-07-06T23:06:40.154278Z","submitted_at":"2026-04-22T04:00:31Z","title":"SMART: A Spectral Transfer Approach to Multi-Task Learning","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-05-10T00:36:38.129904Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.20161"},"observation_digest":"sha256:6afcd1132c4a82a17ebabd4d9831694993edf7c19973177f808f654a97ad312f","observation_id":"b771b0d9-6739-4134-9dc4-383aa8ae8878","resolution":{"observed_at":"2026-05-11T13:46:04.989934Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.22562","last_updated":"2026-04-24T13:55:10Z","snapshot_observed_at":"2026-07-06T23:08:56.419589Z","submitted_at":"2026-04-24T13:55:10Z","title":"Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T12:26:38.542442Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.22562"},"observation_digest":"sha256:14910126b550019a1e198c22a24cfe63496b5127332f8a6f6e72a2f1f3d08051","observation_id":"b421009e-6c48-437d-9b78-f8ea46bfd586","resolution":{"observed_at":"2026-05-11T19:16:08.231877Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.25421","last_updated":"2026-07-02T05:58:48Z","snapshot_observed_at":"2026-08-11T03:41:30.694241Z","submitted_at":"2026-04-28T09:29:41Z","title":"FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T16:51:38.281795Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.25421"},"observation_digest":"sha256:9f1655c4d596f871bc704165aebb01fcfc38ddd44eb0f3f0e17d973971ed45fa","observation_id":"e15ecb3b-1b5b-446a-8d61-955207fa467c","resolution":{"observed_at":"2026-05-11T23:31:15.914030Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.25421","last_updated":"2026-07-02T05:58:48Z","snapshot_observed_at":"2026-08-11T03:41:30.694241Z","submitted_at":"2026-04-28T09:29:41Z","title":"FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-03T00:30:26.465842Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.25421"},"observation_digest":"sha256:01ef9303798cb380808c38ef5195db8fb4e78d53fe0058904d062dc819855f35","observation_id":"6562ea13-6911-4b26-9a95-84f7e33757b4","resolution":{"observed_at":"2026-07-03T00:37:29.591813Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2604.25421","last_updated":"2026-07-02T05:58:48Z","snapshot_observed_at":"2026-08-11T03:41:30.694241Z","submitted_at":"2026-04-28T09:29:41Z","title":"FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-04T01:43:07.236626Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2604.25421"},"observation_digest":"sha256:07c20caf0ab46a0419684da80ee60f49977433e8fddb2db63fdcc5104c74e8ef","observation_id":"89f74fca-d9d2-4261-9f22-84bb5d3f59ae","resolution":{"observed_at":"2026-07-04T01:49:21.563625Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.05959","last_updated":"2026-05-07T10:06:26Z","snapshot_observed_at":"2026-07-06T23:18:31.432422Z","submitted_at":"2026-05-07T10:06:26Z","title":"From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-08T10:54:37.847575Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.05959"},"observation_digest":"sha256:c9b15ee111dd409c9e677fd9354165b51db1215b1ad6b7a2afcbea4e7a7cd35c","observation_id":"a03ff947-abd8-4400-a5b7-ddbd4a3341ce","resolution":{"observed_at":"2026-05-11T19:51:10.810845Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.06446","last_updated":"2026-05-07T15:44:40Z","snapshot_observed_at":"2026-07-06T23:18:55.724335Z","submitted_at":"2026-05-07T15:44:40Z","title":"FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T12:48:14.658236Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.06446"},"observation_digest":"sha256:6b9b38e78ce6b2c8b84c33f6540e1b2932c0af9946334267375a64d4c143d6d8","observation_id":"1b46ca56-27f9-44af-9d67-544924272003","resolution":{"observed_at":"2026-05-11T19:01:19.612335Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.13434","last_updated":"2026-05-13T12:27:22Z","snapshot_observed_at":"2026-07-06T23:25:01.951546Z","submitted_at":"2026-05-13T12:27:22Z","title":"Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-14T19:31:12.149482Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.13434"},"observation_digest":"sha256:8e99f457c8b9acfcc0d9cde68eb03987780416dc800dc245ac1fd87257ed3c38","observation_id":"ca69f770-24ae-42f0-9231-8d726e0ca2b4","resolution":{"observed_at":"2026-05-14T19:32:50.974210Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.14553","last_updated":"2026-05-14T08:31:17Z","snapshot_observed_at":"2026-07-06T23:25:58.895612Z","submitted_at":"2026-05-14T08:31:17Z","title":"Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T01:50:21.013336Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.14553"},"observation_digest":"sha256:74e7705a83bccf1d38f49311ab1bb2fa6f46c5bd393327e0bdb6ff78432f9643","observation_id":"c84e5a03-b9fe-4b33-b5e9-03701ad70099","resolution":{"observed_at":"2026-05-15T01:53:29.078725Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.19969","last_updated":"2026-05-26T08:26:16Z","snapshot_observed_at":"2026-08-03T18:20:15.146044Z","submitted_at":"2026-05-19T15:17:01Z","title":"Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-20T07:34:26.142337Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.19969"},"observation_digest":"sha256:cd12fac7c0e096e4d88360d87b095a3d4601ca520c1a75787b9e8b634b6ea6fd","observation_id":"6d5f4e8a-c35e-41e0-bf27-cd62e517770f","resolution":{"observed_at":"2026-05-20T07:38:09.565766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.19969","last_updated":"2026-05-26T08:26:16Z","snapshot_observed_at":"2026-08-03T18:20:15.146044Z","submitted_at":"2026-05-19T15:17:01Z","title":"Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-30T18:24:37.359481Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.19969"},"observation_digest":"sha256:c65de1b0f4f652c3900cf256b82592c825b8c108d84629c7253cda286829f8f6","observation_id":"dc829208-38a3-4894-8406-35da74fa273a","resolution":{"observed_at":"2026-06-30T18:24:59.972511Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.21317","last_updated":"2026-05-20T15:47:11Z","snapshot_observed_at":"2026-07-06T23:31:46.877766Z","submitted_at":"2026-05-20T15:47:11Z","title":"CRAFT: Conflict-Resolved Aggregation for Federated Training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T05:47:45.982910Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.21317"},"observation_digest":"sha256:659c1bfb7ee6c0afe4e4ce7dded314b018de1bed36e58b84a098793184b47f0b","observation_id":"6f30b939-91ad-4299-9521-38693284c24a","resolution":{"observed_at":"2026-05-21T05:49:40.863706Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2605.26323","last_updated":"2026-06-05T22:00:22Z","snapshot_observed_at":"2026-08-07T10:22:10.412431Z","submitted_at":"2026-05-25T20:53:05Z","title":"Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T20:08:42.479193Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2605.26323"},"observation_digest":"sha256:118d6f5e12587da330b1c77aab7ca0b4a7c4c80a190d92603485e18284e125f4","observation_id":"c96ee16e-837f-4197-a621-be6800ce89f9","resolution":{"observed_at":"2026-07-01T16:55:50.548354Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.00947","last_updated":"2026-05-31T01:33:11Z","snapshot_observed_at":"2026-08-03T13:44:57.654169Z","submitted_at":"2026-05-31T01:33:11Z","title":"Silent Failures in Federated Personalization of Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T18:01:45.324346Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.00947"},"observation_digest":"sha256:558dc48529a1eccee727b68517bf3ac75cead76526877bff4d97ffc67018897d","observation_id":"4dc2375d-8b50-4ab1-9f5a-1b48f18caf61","resolution":{"observed_at":"2026-06-28T18:02:26.222223Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.01607","last_updated":"2026-06-01T02:58:16Z","snapshot_observed_at":"2026-08-08T19:34:01.958203Z","submitted_at":"2026-06-01T02:58:16Z","title":"FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T15:51:08.954790Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.01607"},"observation_digest":"sha256:22a7f359c6fbc9db202d4b1256dbe5f3d2fee913748e8506dce3d5f4fd2c3a53","observation_id":"44422bc1-3c24-413f-8be0-59e0f6a1f61e","resolution":{"observed_at":"2026-07-01T22:06:15.804676Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.02172","last_updated":"2026-06-01T12:30:53Z","snapshot_observed_at":"2026-08-09T09:44:10.959177Z","submitted_at":"2026-06-01T12:30:53Z","title":"Closing the Alignment-Maturity Gap in Federated Prototype Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T15:55:11.018371Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.02172"},"observation_digest":"sha256:1db0975d8f8265e8d634f94d1a6043fded823517b425c0f4283a1f01a143869c","observation_id":"2c4a05e7-312d-4acd-9873-bf4120129991","resolution":{"observed_at":"2026-07-01T21:56:16.423464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.08521","last_updated":"2026-06-07T08:58:19Z","snapshot_observed_at":"2026-08-02T08:23:54.279769Z","submitted_at":"2026-06-07T08:58:19Z","title":"Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-27T18:08:02.927134Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.08521"},"observation_digest":"sha256:0d02058ae789a7fb75faedfb603d5b000c009d7aae9cd056c34b9634bc8e5cb4","observation_id":"733a0f76-2d53-48cb-841f-313068ec5f7a","resolution":{"observed_at":"2026-07-02T23:37:27.351108Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-07-15T10:46:07.438824Z","title":"LEAF: A benchmark for federated settings","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.16304","last_updated":"2026-06-15T07:07:55Z","snapshot_observed_at":"2026-08-09T15:59:10.754869Z","submitted_at":"2026-06-15T07:07:55Z","title":"pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-15T10:46:07.438824Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.16304"},"observation_digest":"sha256:2ee98d49c092b041c9bdeee9ca4dd158ed9dfa31fca4c1e3b78ac73791e539a2","observation_id":"67e26967-529b-413e-889d-92267014d6d7","resolution":{"observed_at":"2026-07-15T10:46:07.438824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.25533","last_updated":"2026-06-24T08:08:10Z","snapshot_observed_at":"2026-08-06T23:51:47.345747Z","submitted_at":"2026-06-24T08:08:10Z","title":"Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems","version":1},"reference_index":116,"source":"pdf_text","source_observed_at":"2026-06-25T20:56:20.603088Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.25533"},"observation_digest":"sha256:ab8a80ca98ad0f32f7dfde2e55c1680e432393ec2b695eb48e4d272633c922c9","observation_id":"c122ea5b-317a-4f58-a95e-faf826d69544","resolution":{"observed_at":"2026-07-04T20:00:07.845935Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2606.31742","last_updated":"2026-06-30T14:35:45Z","snapshot_observed_at":"2026-08-07T17:03:47.011619Z","submitted_at":"2026-06-30T14:35:45Z","title":"FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-01T05:59:16.095114Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2606.31742"},"observation_digest":"sha256:892e8a120dd274e846ba2f2637dcf5814aea054b18b72ddeeb43768747af542c","observation_id":"aaae2aa9-f416-412e-95fb-4dc7e4b35cb9","resolution":{"observed_at":"2026-07-01T09:55:41.357182Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-07-12T08:12:59.937555Z","title":"‘‘Leaf: A benchmark for federated settings’’","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.02636","last_updated":"2026-07-02T15:32:37Z","snapshot_observed_at":"2026-08-07T05:23:12.765898Z","submitted_at":"2026-07-02T15:32:37Z","title":"Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T08:12:59.937555Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.02636"},"observation_digest":"sha256:0135e9a836e7be3150abe3d231be2e72051d11097149089973626f61cdded69a","observation_id":"dc795426-baee-4eb6-ba77-cbaa5605edbf","resolution":{"observed_at":"2026-07-12T08:12:59.937555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-07-11T21:13:04.339445Z","title":"Caldas, S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.04170","last_updated":"2026-07-05T08:33:38Z","snapshot_observed_at":"2026-08-07T13:07:32.962059Z","submitted_at":"2026-07-05T08:33:38Z","title":"FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-11T21:13:04.339445Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.04170"},"observation_digest":"sha256:f703e1a79afa4b9103f771167f9142a04f24a4e74a2a5013347a03b3ec9ff4ed","observation_id":"5e245410-3b2c-4986-8f5b-b0876e1550d4","resolution":{"observed_at":"2026-07-11T21:13:04.339445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-07-11T21:05:03.994941Z","title":"Leaf: A benchmark for federated settings,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.04189","last_updated":"2026-07-05T09:23:35Z","snapshot_observed_at":"2026-08-08T08:28:57.746527Z","submitted_at":"2026-07-05T09:23:35Z","title":"SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-11T21:05:03.994941Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.04189"},"observation_digest":"sha256:d1440b816197e54fb5f8908bba2fed51885be05559a3bb832cc7b4dd5f0c6eda","observation_id":"23e70ba4-5ab8-4513-aa24-d3dd6ca5fa00","resolution":{"observed_at":"2026-07-11T21:05:03.994941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":"1812.01097","doi":"10.48550/arxiv.1812.01097","metadata_source":"pith","pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leaf: A benchmark for federated settings","venue":"cs.LG","work_id":"94ec8be1-6cf1-4962-994c-e762a0b9c3ee","year":2018},"citing_paper":{"arxiv_id":"2607.06612","last_updated":"2026-07-07T07:10:43Z","snapshot_observed_at":"2026-08-07T19:18:13.548730Z","submitted_at":"2026-07-07T07:10:43Z","title":"PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-11T01:51:15.447661Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.06612"},"observation_digest":"sha256:55bece53afb0950fbad875023711b65702be587cb2db0d04473d95750d1b2d66","observation_id":"55e3e192-fdd6-4780-8726-753b80b64180","resolution":{"observed_at":"2026-07-11T01:57:52.301024Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-07-13T07:32:39.495351Z","title":"LEAF: A benchmark for federated settings.arXiv preprint arXiv:1812.01097, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.08784","last_updated":"2026-06-13T10:32:34Z","snapshot_observed_at":"2026-08-06T10:06:32.747614Z","submitted_at":"2026-06-13T10:32:34Z","title":"HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T07:32:39.495351Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.08784"},"observation_digest":"sha256:5d358da1fc7a7eb58786d2677a8dc504bda1e85668ed39ea440d140f99ebdca2","observation_id":"f2604187-4c35-4345-817d-771b5fae4c38","resolution":{"observed_at":"2026-07-13T07:32:39.495351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-02T00:29:02.071069Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15052","last_updated":"2026-07-16T14:30:53Z","snapshot_observed_at":"2026-08-10T17:01:34.537619Z","submitted_at":"2026-07-16T14:30:53Z","title":"NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T00:29:02.071069Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.15052"},"observation_digest":"sha256:2e5379ac70ee5afd1845e4c53b8c01017840e3ee060e2a71f22f6a5c6b6b2b50","observation_id":"e2604652-6362-45ba-9b4b-03a47f9d85c3","resolution":{"observed_at":"2026-08-02T00:29:02.071069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-01T16:41:36.776273Z","title":"CoRR (2018),http://arxiv.org/abs/ 1812.01097","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.17913","last_updated":"2026-07-20T13:08:41Z","snapshot_observed_at":"2026-08-05T06:43:05.293637Z","submitted_at":"2026-07-20T13:08:41Z","title":"AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T16:41:36.776273Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.17913"},"observation_digest":"sha256:74d1aee992b18df6c4e81c349755e4858124e3d04ee821e8f1ee30ebceb30d6f","observation_id":"f626d505-2e12-4b89-bff8-143c3d80ab8d","resolution":{"observed_at":"2026-08-01T16:41:36.776273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-01T02:30:36.379602Z","title":"arXiv preprint arXiv:1812.01097 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.25441","last_updated":"2026-07-28T08:33:04Z","snapshot_observed_at":"2026-08-04T17:19:33.300116Z","submitted_at":"2026-07-28T08:33:04Z","title":"PIcsC: Partitioning-Induced Covariate Shift Correction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T02:30:36.379602Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2607.25441"},"observation_digest":"sha256:4a6421f9979e3d896e321e1d003a833d06ac061760357f5158befb8a0f3067ad","observation_id":"9c4f790b-babc-4aa7-b6e0-d8c0c247445f","resolution":{"observed_at":"2026-08-01T02:30:36.379602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-06T00:30:52.792258Z","title":"ArXivabs/1812.01097(2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.01197","last_updated":"2026-08-02T12:23:45Z","snapshot_observed_at":"2026-08-10T17:49:24.377904Z","submitted_at":"2026-08-02T12:23:45Z","title":"Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T00:30:52.792258Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2608.01197"},"observation_digest":"sha256:a9ab4dc5ff52f830864708754ec0eca41b5614769677303dc3c95d1da5bf9116","observation_id":"f44cd088-cc9f-4db2-9969-20a237e15a57","resolution":{"observed_at":"2026-08-06T00:30:52.792258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-10T13:52:51.809854Z","title":"LEAF: A benchmark for federated settings,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.07157","last_updated":"2026-08-07T12:26:03Z","snapshot_observed_at":"2026-08-11T08:10:50.743095Z","submitted_at":"2026-08-07T12:26:03Z","title":"Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T13:52:51.809854Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2608.07157"},"observation_digest":"sha256:22a9f47089072430d47f2b69282cd9f15d4928a28961cef458ef547ceb2984ea","observation_id":"4620ed8a-f24e-469d-8bb6-376bef9dc983","resolution":{"observed_at":"2026-08-10T13:52:51.809854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1812.01097/citation-record","integrity":"/paper/1812.01097/integrity","json":"/paper/1812.01097/citation-record.json","paper":"/paper/1812.01097"},"outbound":[],"paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T00:22:10.318064Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 70 inbound Pith citation observations for arXiv:1812.01097."}