{"as_of":"2026-08-07T06:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ee2a94709b20e13393df21040bca693dcdad60f0eff3852e8cb4daa607351a78","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:10:59.966903Z","state":"measured"},{"denominator":62,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":62,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.03973/citation-record","integrity":"/paper/2507.03973/integrity","json":"/paper/2507.03973/citation-record.json","paper":"/paper/2507.03973"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.455217Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"bd8ee5db-082d-42c8-95f6-9a17699f65e7","year":2017},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.533861Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:991e2babe28d83665adb406debc3810daf2378d5fc4fa598df44e08ff25ae6e5","observation_id":"993d0ab3-31b8-4909-adcb-ee3ecdc1e6cc","resolution":{"observed_at":"2026-08-06T20:11:03.459893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.444572Z","title":"Federated learning for the Internet of Things: Applications, challenges, and opportunities,","venue":null,"work_id":"9b0fab01-2a66-4b48-83fc-08e24ee78a90","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.595568Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:69a40feb11d5c6a8dfc4456ee5a436ce794092ed493a4972d5d3b9d386704e09","observation_id":"c1c4aeaf-22de-43f5-b8c6-f6d32e380f06","resolution":{"observed_at":"2026-08-06T20:11:03.448167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.433400Z","title":"Confederated learning: Federated learning with decentralized edge servers,","venue":null,"work_id":"65760025-7bb6-4267-b9c5-3cfacdae306a","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.692592Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:5fea57a85c3866cfda255dd543eea732725a54799ad8aa58bb56c8e86320e7df","observation_id":"1e6ee25c-cae0-4341-aea9-097e416315ec","resolution":{"observed_at":"2026-08-06T20:11:03.436919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.421428Z","title":"A survey on federated learning,","venue":null,"work_id":"9fe4cdfa-9215-4cfe-845f-b44d0c5ca19c","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.787154Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:017a4fad98cf2efe54b79addcae7216dd35bacaa6ea14528fe79da1063d794e2","observation_id":"59654741-e401-425a-92bc-0b53ec26cf30","resolution":{"observed_at":"2026-08-06T20:11:03.424847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.409101Z","title":"Heterogeneous feder- ated learning: State-of-the-art and research challenges,","venue":null,"work_id":"37d694ed-f2ad-4b91-a146-65e376a825ba","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.878740Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e548e17f78a63d4ddc5963a046340616bbe02236d77caa264727dd78aec9e582","observation_id":"be144260-4682-4f14-9eb7-22b7af74628f","resolution":{"observed_at":"2026-08-06T20:11:03.413512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.393116Z","title":"FedPD: A federated learning framework with adaptivity to Non-IID data,","venue":null,"work_id":"02f37782-fe41-4f64-9454-899f854beb7a","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:54.949577Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e36fe2737aaf400642837a20e20dd14713bc27ee3daf69cce6280801db606c66","observation_id":"6f99b6ca-d81c-487f-93f5-7a0f9f3f7bfb","resolution":{"observed_at":"2026-08-06T20:11:03.399154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.378734Z","title":"Towards personalized federated learning,","venue":null,"work_id":"466a2ae6-c978-4a03-83a2-e45ed9cdf609","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.021949Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:a4adf8b29e639b5cca7d295799987e6c9baf6f1762adbf4ac45dc16d598555ac","observation_id":"09a99ebc-465a-43cd-ad98-ac2553936df4","resolution":{"observed_at":"2026-08-06T20:11:03.382909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.361725Z","title":"Byzantine-robust and communication-efficient personalized federated learning,","venue":null,"work_id":"2ba081dd-4c52-4ecc-a994-e5835b741161","year":2025},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.088839Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:d875f3eb99f4894bd940352ec7dd87902d6359e6c6c874518dc3de76878de768","observation_id":"26ed460c-8cf2-470a-9d23-ba754f4d6a96","resolution":{"observed_at":"2026-08-06T20:11:03.367570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.349574Z","title":"Adaptive model pruning and personalization for federated learning over wireless networks,","venue":null,"work_id":"a2cf6b06-0b6f-4545-bd59-218caeae5f2b","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.169911Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e96edc68a306a70bbfaeccd3846362b04e2fd3e72b84e545aafe7d458ee0713e","observation_id":"4819e27b-9065-448e-9009-d2f525cd02c4","resolution":{"observed_at":"2026-08-06T20:11:03.353527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.337621Z","title":"Personalized federated learning towards communication efficiency, robustness and fairness,","venue":null,"work_id":"3585349a-b441-42e0-9911-f046e7895b66","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.247820Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:a4987cab67c2fe6e562c91d78d681cda20bd10a62f6479cf4c6dad486647fe58","observation_id":"4b6fefa9-01b3-4b69-b1a5-19af753314d4","resolution":{"observed_at":"2026-08-06T20:11:03.341505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:10:55.322851Z","title":"Communication-efficient design for quantized decentralized federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.322851Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:5d97ec19975707f3aca633d7d82c84d7fecc53e2f3a1770ad308a94936dd08e3","observation_id":"377dd350-ed6f-4551-88e9-f996b0ef425c","resolution":{"observed_at":"2026-08-06T20:10:55.322851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05242","last_updated":"2024-08-06T22:28:13Z","snapshot_observed_at":"2026-07-06T18:59:00.983278Z","submitted_at":"2024-08-06T22:28:13Z","title":"FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG","version":1},"cited_work":{"arxiv_id":"2408.05242","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.05242","snapshot_observed_at":"2026-08-06T20:11:00.513165Z","title":"FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG","venue":"cs.LG","work_id":"dccb8836-9807-4054-9f82-2c5929e7e975","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.368968Z"},"links":{"cited_paper":"/paper/2408.05242","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:ff41cc2332afe3f60365f09bda3cd9136e67080db52e83269a118ac2ea308452","observation_id":"c776c5aa-152e-45ed-870a-f52a7f5832fa","resolution":{"observed_at":"2026-08-06T20:11:00.570927Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.316464Z","title":"A survey of trustworthy federated learning: Issues, solutions, and challenges,","venue":null,"work_id":"2687be2d-de51-4000-81fd-6d2f97429584","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.433398Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:28915f4bd59525d566cb45a02589e4c0132559f5507bce9ad3b626ef05104da8","observation_id":"474a43e3-41a0-4c28-8b83-99a0f3037f2d","resolution":{"observed_at":"2026-08-06T20:11:03.320718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.303053Z","title":"An experimental study of Byzantine- robust aggregation schemes in federated learning,","venue":null,"work_id":"928937f0-b2a8-43b9-9b59-2490d27250e6","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.503624Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:34fff9b13819fa20898e390f45a6ae0e42ebb554431d6734ab0841a00038de18","observation_id":"5cd67306-11e3-4dc8-afda-1e8b53be149b","resolution":{"observed_at":"2026-08-06T20:11:03.307245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.291417Z","title":"A comprehensive survey of privacy- preserving federated learning: A taxonomy, review, and future direc- tions,","venue":null,"work_id":"3609f038-f79c-4b6e-9913-5fa81d25e611","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.583144Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:1619ccbf5d46068a1bb034e14dbbc7244c7946c5d740582a19378c54039f96dd","observation_id":"141bd45f-cdb5-4718-a947-d0eef873210c","resolution":{"observed_at":"2026-08-06T20:11:03.295547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.280070Z","title":"signSGD: Compressed optimisation for non-convex problems,","venue":null,"work_id":"3bf8d9bf-0858-4e2c-a222-986b3e0ff004","year":2018},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.662322Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:a1c22ed2d484ea03f82eafb6113f1920ed7aaf8b2ff41b439250b8c0d1fc828f","observation_id":"8f64aee9-5a1d-4303-8ef6-fa6a4dd68889","resolution":{"observed_at":"2026-08-06T20:11:03.283940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05291","last_updated":"2019-02-22T19:55:48Z","snapshot_observed_at":"2026-07-06T07:07:34.689643Z","submitted_at":"2018-10-11T23:50:32Z","title":"signSGD with Majority Vote is Communication Efficient And Fault Tolerant","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05291","snapshot_observed_at":"2026-08-06T20:10:55.711188Z","title":"signSGD with majority vote is communication efficient and fault tolerant,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.711188Z"},"links":{"cited_paper":"/paper/1810.05291","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:7440c51a7af03fa6be98c4b159937f0123e40421f5a0d5789a0bc262fc8f50c2","observation_id":"36a113d1-a4ec-448b-9ac2-eba98b3de409","resolution":{"observed_at":"2026-08-06T20:10:55.711188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.269465Z","title":"Distributed training with heterogeneous data: bridging median- and mean-based algorithms,","venue":null,"work_id":"3489d569-b208-4a69-b07a-566748474e26","year":2020},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.752847Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:1be5fdcd0ba086e0c5f3db80fd0978b0887035de174485a2c23c24a5de42ccc6","observation_id":"8e3a937e-a246-4927-8470-9066270d666e","resolution":{"observed_at":"2026-08-06T20:11:03.273582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.258652Z","title":"Sign-based gradient descent with heterogeneous data: Convergence and Byzantine resilience,","venue":null,"work_id":"fcd23d23-d8d2-4bf4-90ff-3368faa817c9","year":2025},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.820749Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:f6dc143f788acf6eea0a7f4e9e03d2e4b53f18b2428d72ecdb6adc962db8d68c","observation_id":"15618c20-38fd-42bc-b163-8a8f410a6b19","resolution":{"observed_at":"2026-08-06T20:11:03.262387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.247981Z","title":"z-SignFedAvg: a unified stochastic sign-based compression for federated learning,","venue":null,"work_id":"6ae6b0e9-d0ab-41a0-b5a8-4526508ab3be","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.870023Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:cce6fb78c6bf65370ceed3c64230a1b517254daa659a5655788337215fdd4318","observation_id":"9835f748-50d8-4c25-9c2c-d1df812bd609","resolution":{"observed_at":"2026-08-06T20:11:03.251113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.236851Z","title":"S 3GD-MV: Sparse-SignSGD with majority vote for communication-efficient distributed learning,","venue":null,"work_id":"27b41936-128b-406e-8fa8-28ac2833c6ea","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:55.943030Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:ce555275845b879234e055253d9780267b156236a03f0f1372924bd2f788aefc","observation_id":"721e3009-e5e0-478e-90fa-8678015c5e92","resolution":{"observed_at":"2026-08-06T20:11:03.240298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.226726Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"88d9b368-a6b5-444e-8258-ba17d7d0a947","year":2020},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.008461Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:2b22cfd42e63598bd7bb8a52c5065ab00ab2cf32b6d4c407a2350ceacade9f1a","observation_id":"b3b1607b-9981-4238-8d67-05ece76eea8b","resolution":{"observed_at":"2026-08-06T20:11:03.230343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.05492","last_updated":"2017-10-30T20:52:14Z","snapshot_observed_at":"2026-07-06T05:15:00.158639Z","submitted_at":"2016-10-18T09:11:51Z","title":"Federated Learning: Strategies for Improving Communication Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.05492","snapshot_observed_at":"2026-08-06T20:10:56.075810Z","title":"Federated learning: Strategies for improving communication efficiency,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.075810Z"},"links":{"cited_paper":"/paper/1610.05492","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:790aaf9a4dacf62da1498ad6e9e1af2225fb8843e45ac578a41e82218616fb50","observation_id":"03eac1a6-31db-4051-a066-b3480d059c08","resolution":{"observed_at":"2026-08-06T20:10:56.075810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.216653Z","title":"QSGD: Communication-efficient SGD via gradient quantization and encoding,","venue":null,"work_id":"9e9203ba-0da1-4bb3-a3f7-c2fd9fa7f99b","year":2017},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.129715Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e4e66e4099a73b26f4c4d2f41be7525c38dfa6058eb61de066c1f3f15cb59823","observation_id":"66e55aca-81fe-4647-b28c-09b2f86d421e","resolution":{"observed_at":"2026-08-06T20:11:03.219880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.205855Z","title":"UVeQFed: Universal vector quantization for federated learning,","venue":null,"work_id":"98206766-35a4-48b0-ac3a-76e53b6a598a","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.170429Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:7645f16436fcccd2d96762a50bfcf0ff49c3354ac87c3f5f3f7144f7416b809c","observation_id":"cf2d074b-f0c1-4bc9-a16f-934f2d016a59","resolution":{"observed_at":"2026-08-06T20:11:03.209228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.195794Z","title":"Adaptive gradient quantization for data-parallel SGD,","venue":null,"work_id":"c3446e1d-23a4-4d56-be74-59938ea67af3","year":2020},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.252726Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:b1cc7522019f921d5ec92974f026f792046c90976a413f5097532f2676dfba7a","observation_id":"eedf6339-a524-4c49-b692-adaf0bd346ed","resolution":{"observed_at":"2026-08-06T20:11:03.199392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.184690Z","title":"Communication-efficient federated learning with adaptive quantiza- tion,","venue":null,"work_id":"70af748d-2667-4dfd-acf2-d00a0931dcd2","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.341387Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e55517ed5e80063d8b0c4bb380a9093a14bad06103effa53e2627aa47ca7ea79","observation_id":"3c1ed7b3-3958-473a-a5f8-d6cc4f36f877","resolution":{"observed_at":"2026-08-06T20:11:03.188676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08977","last_updated":"2024-08-16T19:00:36Z","snapshot_observed_at":"2026-07-06T19:01:45.542387Z","submitted_at":"2024-08-16T19:00:36Z","title":"FedFQ: Federated Learning with Fine-Grained Quantization","version":1},"cited_work":{"arxiv_id":"2408.08977","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.08977","snapshot_observed_at":"2026-08-06T20:11:00.305015Z","title":"FedFQ: Federated Learning with Fine-Grained Quantization","venue":"cs.DC","work_id":"70956a8e-3904-4381-acef-5625e115ff0b","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.393617Z"},"links":{"cited_paper":"/paper/2408.08977","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:ef3f2e6c9715082e28e9f2c8a76955c69faa4fd7ed7e5f10a1b442d0b94465ee","observation_id":"c178f8d0-8b2d-4c59-bb91-0b96b450a43e","resolution":{"observed_at":"2026-08-06T20:11:00.389558Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.172102Z","title":"Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehicle edge computing,","venue":null,"work_id":"d9ff0bf0-5087-4a90-8e90-f2b81cc7bad8","year":null},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.485358Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:24e9858b9ff3017399e0d35ed032cdd67ff0d4cd2033994fa93439b05fe14994","observation_id":"dd41c9e3-0f0e-42fd-a548-ada942409148","resolution":{"observed_at":"2026-08-06T20:11:03.177238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.159919Z","title":"Joint accuracy and latency optimization for quantized federated learning in vehicular networks,","venue":null,"work_id":"e7533f26-f18c-44a6-9dcd-7050f175551e","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.573116Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:7498adedee4944b009df0bf41e24525eb9afcfd47fdb94be80e66f5cdb430584","observation_id":"2969dbb4-26e3-4d7b-ba02-841de7ecff2e","resolution":{"observed_at":"2026-08-06T20:11:03.164061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.148710Z","title":"The algorithmic foundations of differential privacy,","venue":null,"work_id":"0434ce97-6a8e-40b0-8a27-2418b7e2ce11","year":2014},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.656474Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:31b2cb088e60a6041fe4002cb39c029aeb6ed0b26f1762923f3bfb3f92d95892","observation_id":"dbdf19c3-7cf0-4cd8-9de1-f3c753f630d6","resolution":{"observed_at":"2026-08-06T20:11:03.152368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.135105Z","title":"A survey on security and privacy of federated learning,","venue":null,"work_id":"08195711-931f-4a9b-a8cd-8f63feabddff","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.770901Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:ea583987637afb471dcab4f7e99c58097c300a8975bbe35832031d548d36d50d","observation_id":"3f23b934-92c2-474d-a608-5a995191eade","resolution":{"observed_at":"2026-08-06T20:11:03.138838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.07557","last_updated":"2018-03-01T10:12:27Z","snapshot_observed_at":"2026-07-06T06:15:22.497001Z","submitted_at":"2017-12-20T16:28:37Z","title":"Differentially Private Federated Learning: A Client Level Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.07557","snapshot_observed_at":"2026-08-06T20:10:56.871054Z","title":"Differentially private federated learning: A client level perspective,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.871054Z"},"links":{"cited_paper":"/paper/1712.07557","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:fef301626a2f46c12daa173ede9f689b7a5a0006057a3a9773b30e2ef2f8f572","observation_id":"7d0775e9-3344-4ff9-908a-bf159ae6e391","resolution":{"observed_at":"2026-08-06T20:10:56.871054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.123618Z","title":"cpSGD: communication-efficient and differentially-private distributed SGD,","venue":null,"work_id":"06e25bf3-f1b3-4c28-bae5-293ad06fdcff","year":2018},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:56.974900Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:a22e5d87176f00a71547af041dbfa0d6489b7c2ce0aae5255e24d034e5ed8f2f","observation_id":"2fcd9125-ea0e-4bce-99d2-5decc0d7ee5b","resolution":{"observed_at":"2026-08-06T20:11:03.127670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.112041Z","title":"The Skellam mechanism for differentially private federated learning,","venue":null,"work_id":"8c4a57aa-74e2-479b-81fb-605788af3d78","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.058426Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:486e49a9aa9f5219447c6691b84ef312ea7c182631841e33e89bb5a2066ef4ff","observation_id":"584252c9-5e26-49be-8edb-5be0288a20f4","resolution":{"observed_at":"2026-08-06T20:11:03.116049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.099947Z","title":"The distributed discrete Gaussian mechanism for federated learning with secure aggregation,","venue":null,"work_id":"72548590-a93c-41df-bf6b-dd606e57f637","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.110423Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:5a5302ec850c11cfe3a402300c3282aaae4250831c700a51a2e9dfebf4baa159","observation_id":"89da891c-ee5f-4be9-ad1e-ef8c72f02ac1","resolution":{"observed_at":"2026-08-06T20:11:03.103513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00972","last_updated":"2019-12-06T18:35:54Z","snapshot_observed_at":"2026-07-06T08:34:24.211365Z","submitted_at":"2019-11-03T21:19:13Z","title":"Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00972","snapshot_observed_at":"2026-08-06T20:10:57.232378Z","title":"Privacy for free: Communication- efficient learning with differential privacy using sketches,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.232378Z"},"links":{"cited_paper":"/paper/1911.00972","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:3ac68f64ca087c14e8190f60fa4c55aee6fac5ede4dc14c9ed7e00ffd54300b9","observation_id":"e0ab9fc4-0ff2-4054-9768-2f1054222e22","resolution":{"observed_at":"2026-08-06T20:10:57.232378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.086408Z","title":"Joint privacy en- hancement and quantization in federated learning,","venue":null,"work_id":"b4388cab-9bc7-406e-9fc4-703efa2c9286","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.298586Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:537c362e58b10da8e5fcd80414f9612bd80fd2f84862fa09e72af443f2fb7aca","observation_id":"f9009b94-b4e4-4548-b4f5-b974ad5ed36a","resolution":{"observed_at":"2026-08-06T20:11:03.091411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11913","last_updated":"2023-06-20T21:54:13Z","snapshot_observed_at":"2026-08-05T17:30:38.237528Z","submitted_at":"2023-06-20T21:54:13Z","title":"Randomized Quantization is All You Need for Differential Privacy in Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11913","snapshot_observed_at":"2026-08-06T20:10:57.412445Z","title":"Randomized quantization is all you need for differential privacy in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.412445Z"},"links":{"cited_paper":"/paper/2306.11913","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:4529f49293a05557f661267fd07985178ba1d0f466a8cc2ab80f265000ecff7a","observation_id":"c68631fb-2acb-4b8d-9d42-750d7da28d27","resolution":{"observed_at":"2026-08-06T20:10:57.412445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.071785Z","title":"vqSGD: Vector quantized stochastic gradient descent,","venue":null,"work_id":"2de50466-f7f4-4b13-8ff5-7ceb81bfc596","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.530261Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:eba73ab93d187206d20ab862dd706c53074745f018b079cf1775cde0c583b4a3","observation_id":"d7b538e7-40fb-4019-80c1-73f8638f6ab2","resolution":{"observed_at":"2026-08-06T20:11:03.075851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.058790Z","title":"Machine learning with adversaries: Byzantine tolerant gradient descent,","venue":null,"work_id":"73ed3517-b528-4d56-b64e-916745c9b062","year":2017},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.637789Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:9c9136f5c5bb6127c219c45bf38cc2e1a29165efc43b6a6c919240d309789671","observation_id":"1644fa9c-582f-4384-bed4-eb7a15257e1f","resolution":{"observed_at":"2026-08-06T20:11:03.062390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.046657Z","title":"The hidden vulner- ability of distributed learning in Byzantium,","venue":null,"work_id":"8dd6c3db-49d2-49b2-950a-cdbcf40900c9","year":2018},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.757035Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:9d6accd9cd2980469c1e25416844a9d338b8a7209b695c77674b6fa485af0e66","observation_id":"aed3f6a9-c627-446b-a196-33919dc51759","resolution":{"observed_at":"2026-08-06T20:11:03.049993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.036995Z","title":"FABA: an algorithm for fast aggregation against Byzantine attacks in distributed neural networks,","venue":null,"work_id":"9f017280-6af1-4b8f-8d78-964c12f9c958","year":2019},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.810904Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:4aa9239541e66a4c247112504cd0a30cd4a122f1cbcca381aea4a7e0ad646f5a","observation_id":"dd049cb7-9c45-49e2-bf34-5a9a0e8122fd","resolution":{"observed_at":"2026-08-06T20:11:03.040191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.026386Z","title":"Byzantine-robust dis- tributed learning: Towards optimal statistical rates,","venue":null,"work_id":"f3df636d-ec4f-44a4-8f96-c94560879bd2","year":2018},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:57.925576Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:865115471142ac9a7442dcb64c33309b9f5ef3ca14a870262bc11e10c97a43c8","observation_id":"2151e563-b5f5-4293-bf40-37429bd6bba6","resolution":{"observed_at":"2026-08-06T20:11:03.030304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.016294Z","title":"Robust aggregation for federated learning,","venue":null,"work_id":"31823373-42de-43de-be26-b56b8651927b","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.009559Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:4da23323ecef059fb6996079bcfb3141b51c28a2c947f3111b232c6144cfea31","observation_id":"1741777a-8b67-4a0f-9d55-46b1ef1b3e4d","resolution":{"observed_at":"2026-08-06T20:11:03.019714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:03.006360Z","title":"DRACO: Byzantine-resilient distributed training via redundant gradients,","venue":null,"work_id":"7c7af4b8-f1d3-4e1d-9b5c-06084f6e446f","year":2018},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.121618Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:076dd35b0bd89d50b5d3b5c234b47289b55d7f49286d3db570f7b61f9224792e","observation_id":"3bf1e52e-eb09-4f63-a5f4-edb0e5e7cd4e","resolution":{"observed_at":"2026-08-06T20:11:03.009734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:02.994435Z","title":"DETOX: a redundancy-based framework for faster and more robust gradient aggregation,","venue":null,"work_id":"752ddeab-6792-4b70-a8f9-a0746422f8df","year":2019},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.279625Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:c13798d5178b87f6e24951d8091e92c3ec9c354980c24b59049bca1a8adec926","observation_id":"27017c96-fb38-4e7b-becc-94ae02052161","resolution":{"observed_at":"2026-08-06T20:11:02.998201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09365","last_updated":"2023-11-22T09:08:15Z","snapshot_observed_at":"2026-08-07T05:21:15.626151Z","submitted_at":"2020-06-16T17:58:53Z","title":"Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09365","snapshot_observed_at":"2026-08-06T20:10:58.404804Z","title":"Byzantine-robust learning on heterogeneous datasets via bucketing,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.404804Z"},"links":{"cited_paper":"/paper/2006.09365","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:d80d481f4c36a237b862ba7d736dccddec775e2bc04cd5b1f40cbe67db149c68","observation_id":"47004a6c-e9cf-426c-ab4a-a6fc8283e435","resolution":{"observed_at":"2026-08-06T20:10:58.404804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:02.942359Z","title":"Byzantine-robust learning on heterogeneous data via gradient splitting,","venue":null,"work_id":"016f38c1-c228-45be-b0c2-bca934827cef","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.499102Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:25d4ff14b0ef3cf114b42caca480ee56a5e0457ae40e4d13b0fd466418949efe","observation_id":"03f665a7-8fae-4030-b12f-c9f7cd59254c","resolution":{"observed_at":"2026-08-06T20:11:02.973694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:02.778647Z","title":"Shielding federated learning: Robust aggregation with adaptive client selection,","venue":null,"work_id":"097b18b7-dfcc-4cb7-ace0-28da228630b4","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.599805Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:0ef5a4f0020f361e75010f7b26c255383e87e0e555195a15ae21d0a745852cad","observation_id":"1be11789-7020-4fce-ae59-2a906597f22e","resolution":{"observed_at":"2026-08-06T20:11:02.857707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:02.459778Z","title":"Learning from history for Byzantine robust optimization,","venue":null,"work_id":"5d3e78c4-89d2-426c-8706-a74a54077ce4","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.689872Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:7a20b2454b576f1d87ca3a69d368a4c60d14e593493263fa787db3b4ba0d41a1","observation_id":"f28693af-3414-49bd-b57a-64526f69acc0","resolution":{"observed_at":"2026-08-06T20:11:02.606928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:02.125978Z","title":"RSA: Byzantine- robust stochastic aggregation methods for distributed learning from heterogeneous datasets,","venue":null,"work_id":"c4f2c3a5-fcad-4d23-ae9e-78bd547330a5","year":2019},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.830671Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:3915535dd328fe344a3ffdcf15ddd4ec7c462d72ffcbf96581a1aa76a75ec628","observation_id":"6eca968e-da75-4a7c-ada7-91f14968c5cb","resolution":{"observed_at":"2026-08-06T20:11:02.277415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05687","last_updated":"2021-12-10T17:31:23Z","snapshot_observed_at":"2026-08-03T11:48:23.252553Z","submitted_at":"2021-12-10T17:31:23Z","title":"Federated Two-stage Learning with Sign-based Voting","version":1},"cited_work":{"arxiv_id":"2112.05687","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.05687","snapshot_observed_at":"2026-08-06T20:11:00.090415Z","title":"Federated Two-stage Learning with Sign-based Voting","venue":"cs.DC","work_id":"8ce749aa-1fe4-446e-9e16-2daa27b2cac6","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:58.917613Z"},"links":{"cited_paper":"/paper/2112.05687","citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:503830c746c918f3d3d3d0f1f4f74c7e5b73aceda84d58f300f61c4de8bca2ef","observation_id":"b6d6fa56-ff21-4123-8966-abae3e3211ff","resolution":{"observed_at":"2026-08-06T20:11:00.151557Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.992323Z","title":"Stochastic sign descent methods: New algorithms and better theory,","venue":null,"work_id":"6ad3adfe-1669-4406-b411-2d64a9308d2f","year":2021},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.046534Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:a253f75072451d6aa694c7fcddee1d04b59a16328a96667a6a366e028edf7f15","observation_id":"ded8ca80-560c-4d0f-aa92-45a267b3dcb7","resolution":{"observed_at":"2026-08-06T20:11:02.047980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.837487Z","title":"Bridging differential privacy and Byzantine- robustness via model aggregation,","venue":null,"work_id":"08f7fa2d-9fd2-475e-9b0f-228d7069ab19","year":2022},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.150706Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:cf4e97caeae4cc96b99eeeeb4c8443fbe2833ded904f9f9132e61afb5bc0dc1d","observation_id":"548a11e9-87af-45c0-b21d-8ec5d8af2ec5","resolution":{"observed_at":"2026-08-06T20:11:01.900715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.666117Z","title":"Federated learning with ℓ1 regularization,","venue":null,"work_id":"9ff5b388-8f46-4456-ab4d-74684cb424cf","year":2023},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.266514Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:b3a603451e7975fdd4721f259f9dc0aba9b75903329c074b5e30c9c533a0707b","observation_id":"be129ca0-9510-4990-a1f2-76a5ff5eedd2","resolution":{"observed_at":"2026-08-06T20:11:01.745093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.500417Z","title":"Mag- nitude matters: Fixing signSGD through magnitude-aware sparsification and error feedback in the presence of data heterogeneity,","venue":null,"work_id":"294bbf4f-cac4-4c8e-b68f-e84f1b20666a","year":2024},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.391233Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:8d5663cf469ebdbfb8b900cd8aaf6aa707409d5deceb91207a218e1450372eef","observation_id":"4547000c-bd3b-4034-b740-f71d22e73986","resolution":{"observed_at":"2026-08-06T20:11:01.570083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.315120Z","title":"Rate distortion for model compression:From theory to practice,","venue":null,"work_id":"731e4cf1-d1e5-432a-8483-b5f3992ca9b8","year":2019},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.514306Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:9aa7d06aadf28d59ead9ebd95264b6d624223be373d65c60aabc127fec4b17b0","observation_id":"06249ae1-99a1-479b-ad62-1cbada1d15bd","resolution":{"observed_at":"2026-08-06T20:11:01.384890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:01.128412Z","title":"Deep learning with differential privacy,","venue":null,"work_id":"ee0f9ccf-db2e-4fe4-a87d-65a834f55b71","year":2016},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.622025Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:32980b2f83caadc4a66155be914a0ba41571bd0f429b9e1e8881c0ac585d73fb","observation_id":"46bf3896-3c3a-408a-9c70-8f1b220a81be","resolution":{"observed_at":"2026-08-06T20:11:01.205254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:00.956492Z","title":null,"venue":null,"work_id":"c89caf4d-a01f-4169-beb9-d44aa1a67511","year":null},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.751241Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:62a7738ad28df8d891e78811d0c4ce90e79631111f47642b847a933a9031329c","observation_id":"cc8423fa-00f9-4a14-9847-4f7140634fe8","resolution":{"observed_at":"2026-08-06T20:11:01.029203Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:00.818399Z","title":null,"venue":null,"work_id":"f0a01ed4-0d41-49b8-a7e4-1cdc9e39b03d","year":null},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.853155Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:d125391170b8e99a01be42cbece6e57b5a2bfa9ffcd8c27479eca4d56c8483ea","observation_id":"cf401d66-ee1a-4ec3-881f-34574705555c","resolution":{"observed_at":"2026-08-06T20:11:00.884882Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:11:00.662949Z","title":"1 M 2 MX m=1 I {cm i = 1} + X i∈B I {zm i > cm i } − X i∈B I {zm i < cm i } ! − M ! bi #2 − θi 2 = E","venue":null,"work_id":"c760da61-a02e-4b06-a687-77647ac5ff48","year":null},"citing_paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:59.966903Z"},"links":{"citing_paper":"/paper/2507.03973"},"observation_digest":"sha256:e916be804c84326ce89992bf3908a6b516e2f03cb3957a399dd55bb80c00397e","observation_id":"5a9041d9-175a-4bd7-8f56-d6649638e360","resolution":{"observed_at":"2026-08-06T20:11:00.724217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.03973","last_updated":"2025-07-05T09:44:05Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-06T19:55:55.455234Z","submitted_at":"2025-07-05T09:44:05Z","title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":3,"verified_fuzzy":50},"total_outbound_references":62},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.03973."}