{"as_of":"2026-08-09T19:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:16191e0f9406f998529b51b85355551244e80d7590edded43255b57943e5ef48","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-09T22:28:05.992944Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.06979/citation-record","integrity":"/paper/2607.06979/integrity","json":"/paper/2607.06979/citation-record.json","paper":"/paper/2607.06979"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T22:36:36.607187Z","title":"M., Sahu, A","venue":null,"work_id":"0ac329b9-fed0-487f-8ab6-01bda68ed929","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:b6986ec602a527f49d00e2487fb88fd9b96a896372d12e3d81b697d7f70ad33d","observation_id":"27f99424-4ae9-4613-9eef-5c697efabd0b","resolution":{"observed_at":"2026-07-09T22:36:36.608463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14390","last_updated":"2022-03-05T20:30:32Z","snapshot_observed_at":"2026-07-06T09:42:35.058716Z","submitted_at":"2020-07-28T17:59:07Z","title":"Flower: A Friendly Federated Learning Research Framework","version":5},"cited_work":{"arxiv_id":"2007.14390","doi":"10.48550/arxiv.2007.14390","metadata_source":"pith","pith_arxiv_id":"2007.14390","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flower: A Friendly Federated Learning Research Framework","venue":"cs.LG","work_id":"396ea1bf-02a2-41cf-8454-93dff73af450","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2007.14390","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:27884192fd8c5a6976844fe6a6906f9fca201c2e113f47320540fcc3970ca0e4","observation_id":"110d89b1-da84-45cc-b65f-c9ef756b57e1","resolution":{"observed_at":"2026-07-09T22:36:36.213359Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.609280Z","title":null,"venue":null,"work_id":"e381c53a-caab-4f09-a884-697dae47c81e","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:0639894cd9c24fcd50ff1a6523d202f189530cc05b7b4f4e86de651b0642858b","observation_id":"6a8fd365-3d3e-430b-86e7-1a094d0c720d","resolution":{"observed_at":"2026-07-09T22:36:36.610584Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.613192Z","title":"S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I","venue":null,"work_id":"4834ff34-03a8-4a52-99fa-861d4d37e368","year":2018},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:7d0ec4860c10a049ae14d09f69c27d4118ef467de817f86adb8b716caae02023","observation_id":"255c908f-40b9-4d9c-8c56-0505b2cea609","resolution":{"observed_at":"2026-07-09T22:36:36.614464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.603232Z","title":null,"venue":null,"work_id":"d6196be1-3c07-44d9-8bc8-0235c80576a0","year":2021},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:32f4ec4746c9477e4924145ef4430fe98849984ba36ebadcc49acfbfd6b349df","observation_id":"72c5fd82-656a-4ed5-8a57-dca1701dc1fe","resolution":{"observed_at":"2026-07-09T22:36:36.604482Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03432","last_updated":"2019-10-08T14:48:43Z","snapshot_observed_at":"2026-08-06T20:25:52.475261Z","submitted_at":"2019-10-08T14:48:43Z","title":"Federated Learning of N-gram Language Models","version":1},"cited_work":{"arxiv_id":"1910.03432","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.03432","snapshot_observed_at":"2026-07-09T22:36:36.207134Z","title":"Federated Learning of N-gram Language Models","venue":"cs.CL","work_id":"130f2e5d-bdf3-401e-aa22-ee9fc34fc139","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1910.03432","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:bba0c4c951fbfb9145372a6b5a748ddc05da1fed242dd6acf1c26c9e81e35171","observation_id":"30e31487-b4a4-47cb-b165-09284aedde97","resolution":{"observed_at":"2026-07-09T22:36:36.208577Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.604944Z","title":"In2020 IEEE International Conference on Big Data (Big Data)(2020), IEEE, pp","venue":null,"work_id":"53229124-bcf9-46ac-a4e0-a947da77cf2d","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:7d7bed9b2767043baea5d8a2921e05ec5530633f3f9d948f77d8ed5d790d901e","observation_id":"35cb766b-337f-478a-9acb-7880ab615030","resolution":{"observed_at":"2026-07-09T22:36:36.606504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.599554Z","title":null,"venue":null,"work_id":"8f279555-5431-4198-83f5-912d78a89c68","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:f05c4eafa1e8ccd127a27f4ed5a9d5985ac0427b816ffa45fe2daba0d893b4c5","observation_id":"267e00ed-0b49-4d2c-af66-435f28a80b6f","resolution":{"observed_at":"2026-07-09T22:36:36.600855Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.597583Z","title":"K., Ingram, S., Mudigere, D., Krishnamoorthi, R., Nair, K., Smelyanskiy, M., and Annavaram, M","venue":null,"work_id":"893ff6e9-f868-4566-8f0b-678748cc9902","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:ae2b9e25f119082f78a9ca0b909da3ce8e5f6d6a62684a25ab6b5c1e9e7d8d96","observation_id":"70fb86dc-99e2-449e-96ee-e4eca5cb47a0","resolution":{"observed_at":"2026-07-09T22:36:36.598959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.595394Z","title":null,"venue":null,"work_id":"3512b653-aca1-4422-aa0b-2c6a48e10980","year":2025},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:0a78940c836d5d71402405c749a619840d658f724f1d81fd9af02c61d414310a","observation_id":"4e13ac51-a93a-42d7-ad7b-3248b0eb1a35","resolution":{"observed_at":"2026-07-09T22:36:36.596905Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1712.07557","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.07557","snapshot_observed_at":"2026-07-09T22:36:36.204150Z","title":"Differentially Private Federated Learning: A Client Level Perspective","venue":"cs.CR","work_id":"9bdfda95-b662-4642-a200-e714da5c0854","year":2017},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1712.07557","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:5774c45ff291544386731638acbf5956cb1d824a7930c7d7de4096f61d89d2c0","observation_id":"8df638de-3020-45c0-881f-ffe099c62c51","resolution":{"observed_at":"2026-07-09T22:36:36.205489Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.601393Z","title":"InProceedings of the Nineteenth European Conference on Computer Systems(2024), pp","venue":null,"work_id":"cf611d21-a6f9-4d9d-bdca-61250ee97801","year":2024},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:08612d86af0a51f7f397a9d9dba7283552e634770edeb984a03dbac0f68b9898","observation_id":"1f90a64b-833d-4b8a-a6d1-23fed077e005","resolution":{"observed_at":"2026-07-09T22:36:36.602714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.03604","last_updated":"2019-02-28T21:07:51Z","snapshot_observed_at":"2026-07-06T07:13:34.019157Z","submitted_at":"2018-11-08T18:37:03Z","title":"Federated Learning for Mobile Keyboard Prediction","version":2},"cited_work":{"arxiv_id":"1811.03604","doi":"10.48550/arxiv.1811.03604","metadata_source":"pith","pith_arxiv_id":"1811.03604","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Federated Learning for Mobile Keyboard Prediction","venue":"cs.CL","work_id":"bee947a7-dcaf-4762-81a5-b75eead51908","year":2018},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1811.03604","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:29a909adae9922d93448fc41c89ad7ccce6351b28cc72760b25add1846ac98eb","observation_id":"4cee5eb6-2e5f-480c-a962-c4fd1ff67504","resolution":{"observed_at":"2026-07-09T22:36:36.202691Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.13518","last_updated":"2020-11-08T19:34:25Z","snapshot_observed_at":"2026-08-03T09:31:17.791596Z","submitted_at":"2020-07-27T13:02:08Z","title":"FedML: A Research Library and Benchmark for Federated Machine Learning","version":4},"cited_work":{"arxiv_id":"2007.13518","doi":"10.48550/arxiv.2007.13518","metadata_source":"pith","pith_arxiv_id":"2007.13518","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fedml: A research li- brary and benchmark for federated machine learning","venue":"cs.LG","work_id":"58736aac-4957-4e2c-affa-ea9a795b1756","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2007.13518","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:bab02c61393055ef5f46a87ac75c798d2b2b28589edc49476bf083014a2a4851","observation_id":"11540710-0762-484c-a5cd-9461e2ad9590","resolution":{"observed_at":"2026-07-09T22:36:36.197323Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.591979Z","title":null,"venue":null,"work_id":"27d74fce-f4fd-4df2-b313-cbd2b36ff222","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:75891fac49235557ff6b415c79d2947360bbab3a8c6ee31290539757a7857f46","observation_id":"378c1a96-b554-4d1c-9fe7-28808b97ac4d","resolution":{"observed_at":"2026-07-09T22:36:36.593169Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.588184Z","title":"In Uncertainty in Artificial Intelligence(2022), PMLR, pp","venue":null,"work_id":"bff9b264-df8f-48cc-8990-95b88786e10e","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:df6ee06c6d1346a2422094f79ccde933948d6f96f7091e616cbc99a962ad1c26","observation_id":"ed0b34fc-e2f2-4caf-a946-cd9c66e5b519","resolution":{"observed_at":"2026-07-09T22:36:36.589588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.590136Z","title":"B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A","venue":null,"work_id":"9ca3b30f-d36a-4be9-bc0b-eed7c0667bfe","year":2021},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:f19b056b5d07bceb196c90b9d02d8dc3dd9a99d4c3208ed32f08612ee8bd9983","observation_id":"5ce3e204-29b6-41e1-a618-bfbb169230b9","resolution":{"observed_at":"2026-07-09T22:36:36.591471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02527","last_updated":"2016-10-08T13:25:15Z","snapshot_observed_at":"2026-07-06T05:13:51.685562Z","submitted_at":"2016-10-08T13:25:15Z","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","version":1},"cited_work":{"arxiv_id":"1610.02527","doi":"10.48550/arxiv.1610.02527","metadata_source":"pith","pith_arxiv_id":"1610.02527","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","venue":"cs.LG","work_id":"d9bc6bd5-0d8b-49f8-bac1-0534db3e670a","year":2016},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1610.02527","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:4fa3f37919c2e1b46fb7c89c08a0145c0ef650cf6b66a9ebb82f91920ac2ddd7","observation_id":"97f94abd-fdf7-453a-8548-ef2e4af8aa73","resolution":{"observed_at":"2026-07-09T22:36:36.200127Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.593721Z","title":null,"venue":null,"work_id":"c42d9ab2-fd56-4ab8-aa51-fe6b7ef4c1ba","year":2009},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:c0dd6b4cd7143700c1e12034d3e54930f85298b466b99dee5935a91e32718f59","observation_id":"4503fdf6-840c-436e-ac3b-b1799068954f","resolution":{"observed_at":"2026-07-09T22:36:36.594836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.611173Z","title":"InInternational conference on machine learning(2022), PMLR, pp","venue":null,"work_id":"034dd795-428f-4c35-9bf8-d1f58c86a35e","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:762de0ba746903dac74f4e7b4cbb0b047d143062b87695f21708ce8c4b5df80a","observation_id":"6ae2799b-8ce1-40fc-af4e-37b17fd1627c","resolution":{"observed_at":"2026-07-09T22:36:36.612612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.578071Z","title":"V., and Chowdhury, M.Oort: Efficient federated learning via guided participant selection","venue":null,"work_id":"2a542104-611b-4685-8d47-0a5682b6091e","year":2021},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:503efeecd03a055bdcec447cdf4d0de70fd1a2d587846c598e9089fbe338d23d","observation_id":"b35f722a-966a-4bd6-b40f-2cd9cedc5ed5","resolution":{"observed_at":"2026-07-09T22:36:36.579273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.01017","last_updated":"2021-05-20T13:25:47Z","snapshot_observed_at":"2026-08-09T04:48:02.309071Z","submitted_at":"2020-10-02T14:09:10Z","title":"Practical One-Shot Federated Learning for Cross-Silo Setting","version":2},"cited_work":{"arxiv_id":"2010.01017","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.01017","snapshot_observed_at":"2026-07-09T22:36:36.193036Z","title":"Practical One-Shot Federated Learning for Cross-Silo Setting","venue":"cs.LG","work_id":"5a29c3dd-e25a-48f4-9b13-32508abdbe06","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2010.01017","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:9835a3014d164ca999cbe18edc2638840c877a70a33a8100fafda6a15e41e0ac","observation_id":"139f7a28-9b87-41db-bb1e-32642507c79b","resolution":{"observed_at":"2026-07-09T22:36:36.194437Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.581893Z","title":"K., Talwalkar, A., and Smith, V.Federated learning: Challenges, methods, and future directions.IEEE signal processing magazine 37, 3 (2020), 50–60","venue":null,"work_id":"cb3f1fb3-7a9a-47b3-94fa-e02725d5e524","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:2e93f18de20dd8c0be462f247675949d18861925b49968b7f568008a5beb7547","observation_id":"acc28beb-6033-4e9a-be76-0ea354780948","resolution":{"observed_at":"2026-07-09T22:36:36.583293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.572337Z","title":"J., et al.Privacy-preserving federated brain tumour segmentation","venue":null,"work_id":"febc118b-f879-4425-8a22-15a32a27c6a4","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:b956926f67c82f65fb62a2400856d8d6d62d58f7ac64ba631bde900525b78b6e","observation_id":"989c58d7-6c0c-47b3-8f87-e7194a213ac6","resolution":{"observed_at":"2026-07-09T22:36:36.573649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02189","last_updated":"2020-06-25T06:45:52Z","snapshot_observed_at":"2026-08-06T15:26:37.394053Z","submitted_at":"2019-07-04T02:04:56Z","title":"On the Convergence of FedAvg on Non-IID Data","version":4},"cited_work":{"arxiv_id":"1907.02189","doi":"10.48550/arxiv.1907.02189","metadata_source":"pith","pith_arxiv_id":"1907.02189","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"On the convergence of FedA vg on non-IID data","venue":"stat.ML","work_id":"74e5da63-cffe-4f1a-a090-e0b98d805e10","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1907.02189","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:1b6086c40f30cd48fe175cbb9ceab50f2d58af64c653db8e5ad60cc34d5572d3","observation_id":"92816ecd-1383-44a4-97ab-c5cf0dbcabe5","resolution":{"observed_at":"2026-07-09T22:36:36.191354Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.574239Z","title":"InInternational Conference on Machine Learning (2018), PMLR, pp","venue":null,"work_id":"cdf02c27-8fd7-4071-98c4-ce0d54dd0edf","year":2018},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:5e93bc17667d8f35e10e5fed66cdf876230430e3b9d734a6565042328ad5d631","observation_id":"85e6ccd5-d63b-4d17-bd74-ef0a5030646c","resolution":{"observed_at":"2026-07-09T22:36:36.575580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.576111Z","title":"A.Mosquitto: server and client implementation of the mqtt protocol","venue":null,"work_id":"0ca17e86-18eb-43af-86c2-a20196db7671","year":2017},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:d35387ee354afdc38f36b482f625b0f10d43d35456ae7d30e4389d5221191728","observation_id":"03d400a5-2d4c-4242-9ad9-84c3641baa2f","resolution":{"observed_at":"2026-07-09T22:36:36.577462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.564296Z","title":"U., and Jaggi, M.Ensemble distillation for robust model fusion in federated learning.Advances in neural information processing systems 33(2020), 2351–2363","venue":null,"work_id":"c1305cfb-978d-4c53-9ed5-be011581a8b3","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:473bcf740a50601727cee59669b049f0bd79e15505ddfcddce517dd295355bec","observation_id":"823d3d89-4827-4174-ad70-aac744904d0a","resolution":{"observed_at":"2026-07-09T22:36:36.566032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.562490Z","title":"K., Ramezani, H., Wang, F., Chen, Z., Dong, Y., Ding, M., Zhao, Z., Zhang, Z., Wen, E., and Eisenman, A","venue":null,"work_id":"9cb42eb4-5da1-4c92-8fe9-aadf7909c914","year":2024},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:2ef1e5d28a6304dc9e3e36c4ab47cf6355a8130a0091f3b344c5a2ab4958e587","observation_id":"9571d3dd-c09d-45e0-a234-2e80025c133c","resolution":{"observed_at":"2026-07-09T22:36:36.563742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.570210Z","title":null,"venue":null,"work_id":"42edbf7f-a0c4-4a95-84a6-e40f1502f33a","year":2017},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:65b87de914b90a1514f84eebb6cc845011e723811dbcdccbae672f4af080f476","observation_id":"f772448a-4756-4b59-ac04-c7d08ed3ed06","resolution":{"observed_at":"2026-07-09T22:36:36.571439Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.568517Z","title":"In2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)(2016), IEEE, pp","venue":null,"work_id":"b5f5c24a-9c7c-4298-9f30-5d4cd25940b1","year":2016},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:6d0889251057c087291b7f5a7e947a417eeaa1165d0b9be0f0cee948ed9e2925","observation_id":"597490ed-5b13-4098-b3ce-2b3216d60413","resolution":{"observed_at":"2026-07-09T22:36:36.569896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.570474Z","title":"{CheckFreq}: Frequent,{Fine-Grained} {DNN} checkpointing","venue":null,"work_id":"fc60b26f-def7-4d43-ba69-a78b572a0d7c","year":2021},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:9252dc143c3eaecb45bcd4c0619e07588b5da756d2ed837d2266f5243490ac64","observation_id":"4d525b08-38d4-44ac-bc50-fe345f0a306f","resolution":{"observed_at":"2026-07-09T22:36:36.571795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.560748Z","title":"C., Pham, Q.-V., Pathirana, P","venue":null,"work_id":"6f1208b1-9c5e-4710-b789-c41aafa34831","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:71c301118ca99e2b4916d797e45d75cd97872c27f78c5ac4bd98d471b56c6ec8","observation_id":"02216052-2949-4245-9319-2795de4bd27c","resolution":{"observed_at":"2026-07-09T22:36:36.561955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.556620Z","title":"In International Conference on Artificial Intelligence and Statistics(2022), PMLR, pp","venue":null,"work_id":"c25e9cc8-04a0-457d-9e15-0b397ac2b666","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:c942b83bcbcae705e1d614059afd9b6902e002bb8e21db4d0082d6b113c68cf3","observation_id":"a69afd9e-c872-4f76-aeac-fc6cfebcb3b0","resolution":{"observed_at":"2026-07-09T22:36:36.558186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.558739Z","title":"InICC 2019-2019 IEEE international conference on communications (ICC)(2019), IEEE, pp","venue":null,"work_id":"0749a590-b2c9-4989-9d6c-f39ef178ef88","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:8a7b9b5b7b4bd997c700942643800322db027aa0778d72f9248e46d31b61141f","observation_id":"341744da-160f-42ca-942d-edb2499ab620","resolution":{"observed_at":"2026-07-09T22:36:36.560214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02054","last_updated":"2025-08-24T12:07:07Z","snapshot_observed_at":"2026-08-03T05:02:32.419407Z","submitted_at":"2019-11-05T19:45:49Z","title":"Federated Adversarial Domain Adaptation","version":3},"cited_work":{"arxiv_id":"1911.02054","doi":null,"metadata_source":"pith","pith_arxiv_id":"1911.02054","snapshot_observed_at":"2026-07-09T22:36:36.178552Z","title":"Federated Adversarial Domain Adaptation","venue":"cs.CV","work_id":"dd2abdc8-c5bd-4989-a27a-69a5088807cd","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1911.02054","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:2e4eb135eaa831fc9ddf78ca89831fb63775326317d0388bd6fdbeb40e8b05ff","observation_id":"e327d421-3822-468c-8748-a700010dd2da","resolution":{"observed_at":"2026-07-09T22:36:36.179896Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_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},"cited_work":{"arxiv_id":"2003.00295","doi":"10.48550/arxiv.2003.00295","metadata_source":"pith","pith_arxiv_id":"2003.00295","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adaptive Federated Optimization","venue":"cs.LG","work_id":"489d9e1f-7fc7-4bb8-aa65-92d6e85bd798","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2003.00295","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:39b06384b5eeb22570a43825b4f961a9b4d23a5bf0c90a819911f94df96bf589","observation_id":"f4ca143a-e93f-45cb-abc6-88103318d889","resolution":{"observed_at":"2026-07-09T22:36:36.182712Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04171","last_updated":"2024-05-07T10:11:42Z","snapshot_observed_at":"2026-08-07T03:20:01.376803Z","submitted_at":"2024-05-07T10:11:42Z","title":"FedStale: leveraging stale client updates in federated learning","version":1},"cited_work":{"arxiv_id":"2405.04171","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.04171","snapshot_observed_at":"2026-07-09T22:36:36.184106Z","title":"FedStale: leveraging stale client updates in federated learning","venue":"cs.LG","work_id":"b13cf9a4-cab6-4394-abe2-94ac93bee23c","year":2024},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2405.04171","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:f408d942b435dc36d2a9d21ba9181f0828a733b6affd55a06d2327cf52113499","observation_id":"a5d63199-8ad7-4183-ae08-f14040bb6a22","resolution":{"observed_at":"2026-07-09T22:36:36.185441Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.612237Z","title":"InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2023), pp","venue":null,"work_id":"be023129-e6ee-44d0-acfb-8e075a228cc5","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:dcb5f1a1ad7e8a8836fe0c62a2b30f96fd87738efabde01480d31d5d542e7bfe","observation_id":"22dcf299-210b-45b0-bbc4-557d62b2e8d3","resolution":{"observed_at":"2026-07-09T22:36:36.613551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.614117Z","title":"In2017 IEEE symposium on security and privacy (SP)(2017), IEEE, pp","venue":null,"work_id":"a1754433-2563-42f4-850c-714d11658c40","year":2017},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:859b71af7a9d323dc62fffb31502e9a87f0c5f94c6e12c68842c8dc30d51fefd","observation_id":"1269da24-074c-4753-bc34-f43b2c6379c7","resolution":{"observed_at":"2026-07-09T22:36:36.615324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.616001Z","title":"In22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)(2025), pp","venue":null,"work_id":"40c17367-dcd9-4b0c-a8e6-24a00ef352e0","year":2025},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:5f0d8836891910d606d5b273557a7feefeed4496c3fcd947f6a1d232fcac40cd","observation_id":"305730bb-1598-4eb7-a1f5-2797ff4bf6c3","resolution":{"observed_at":"2026-07-09T22:36:36.617382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.604299Z","title":"T., Felix, X","venue":null,"work_id":"639af7de-d5d3-468e-85d0-b77f5cf79c84","year":2017},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:6098e7d2ba7b47a5ede46b70e4a58774f8e1a419bda7c4b6ebabd5c4f215eac1","observation_id":"d67697e8-41c5-45f9-8e95-5c6c8d3e9407","resolution":{"observed_at":"2026-07-09T22:36:36.605375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.606292Z","title":null,"venue":null,"work_id":"e4ad8e6c-8901-4643-8f70-7c39718972e7","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:4efbcd0026414ca4c78288bdc0d209f4e7815d7db7ac6b69f0c897cc262606b5","observation_id":"eae24914-eba8-4e99-b21c-86d8ca81fac8","resolution":{"observed_at":"2026-07-09T22:36:36.607422Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06440","last_updated":"2020-02-15T20:09:24Z","snapshot_observed_at":"2026-08-06T14:46:34.371139Z","submitted_at":"2020-02-15T20:09:24Z","title":"Federated Learning with Matched Averaging","version":1},"cited_work":{"arxiv_id":"2002.06440","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.06440","snapshot_observed_at":"2026-07-09T22:36:36.190373Z","title":"Federated learning with matched averaging","venue":"cs.LG","work_id":"51d6e1f4-42a4-4a1d-ab11-01e44c7dff67","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2002.06440","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:a46c270392fafbf2cb841520f9f514dfa2062038a060672755b4c035b8106b6e","observation_id":"7f1cfac9-0c21-4bcc-9e2b-88c044fd4e94","resolution":{"observed_at":"2026-07-09T22:36:36.191928Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.609981Z","title":null,"venue":null,"work_id":"cb3ea2c7-57e6-4af8-b2cf-f851abd476c7","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:32a354747fe9c8e7a6dc428c1848ec77877acdee97f1d0b137d999374eaf5b7c","observation_id":"248a7358-33f7-4901-8f84-42e93a0a51f3","resolution":{"observed_at":"2026-07-09T22:36:36.611358Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.602357Z","title":"V.Tackling the objective inconsistency problem in heterogeneous federated optimization.Advances in neural information processing systems 33(2020), 7611–7623","venue":null,"work_id":"adf3bdb3-c772-4b9b-9cd5-ae6215001d70","year":2020},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:f472e9e1c4170e079ad0008a2b4954b89af7b70d930ab54090af56edc735544e","observation_id":"21f5d8ea-9c42-4b79-93d3-3e9bb635b851","resolution":{"observed_at":"2026-07-09T22:36:36.603645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.608004Z","title":"E., and W ang, Y.Gemini: Fast failure recovery in distributed training with in-memory checkpoints","venue":null,"work_id":"74b2ac4b-0e6a-4358-b52e-965f24e6d951","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:5af09dfd0887a323049078153ea550b716821cc793e85a298a3387c5374062c5","observation_id":"e70adacb-24e3-4e3d-b896-14f2f8243e62","resolution":{"observed_at":"2026-07-09T22:36:36.609422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.600503Z","title":null,"venue":null,"work_id":"f607ac25-aa85-49ac-9243-c3da51acd60f","year":2018},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:9e7351d8499fb5f88f79a1c57fd4d276e9cda65ccdd764f4827ad4ab7b7ac046","observation_id":"94093adc-6a81-4a60-8a63-4e126311289b","resolution":{"observed_at":"2026-07-09T22:36:36.601626Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03934","last_updated":"2020-12-05T01:33:57Z","snapshot_observed_at":"2026-08-09T06:49:53.460129Z","submitted_at":"2019-03-10T06:19:38Z","title":"Asynchronous Federated Optimization","version":5},"cited_work":{"arxiv_id":"1903.03934","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.03934","snapshot_observed_at":"2026-07-09T22:36:36.193284Z","title":"Asynchronous federated optimization","venue":"cs.DC","work_id":"2a4a822b-8793-4cac-ada2-019109747c9a","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/1903.03934","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:fb7f30aba8a34301675e0f4833b03c4f670c41242d31e57a2fb931823ae44fbf","observation_id":"57eb0e34-a3c6-4979-8a66-3ed3e88f0fa7","resolution":{"observed_at":"2026-07-09T22:36:36.194709Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.618197Z","title":null,"venue":null,"work_id":"b636dafb-9a8a-4036-851a-0833975a041c","year":2021},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:af58496641487c752b9f5d1f4e502ea276c9beb2df9ed6b06b9f3bc3e5cfb901","observation_id":"c4a96216-0238-49af-b8ad-798a90e24b12","resolution":{"observed_at":"2026-07-09T22:36:36.619591Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.596549Z","title":"{FwdLLM}: Efficient federated finetuning of large language models with perturbed inferences","venue":null,"work_id":"3cb2440a-c2ce-4d5e-a604-f2dbcb17319e","year":2024},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:82c48033b09030192a3e170504ac30b74791be2b5d3bce8f13a50474aeae0d91","observation_id":"1090f0a1-b2c4-48cb-81ee-8c03f4fcac9d","resolution":{"observed_at":"2026-07-09T22:36:36.597938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18465","last_updated":"2023-07-17T08:28:34Z","snapshot_observed_at":"2026-08-03T01:36:39.426953Z","submitted_at":"2023-05-29T07:54:22Z","title":"Federated Learning of Gboard Language Models with Differential Privacy","version":2},"cited_work":{"arxiv_id":"2305.18465","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.18465","snapshot_observed_at":"2026-07-09T22:36:36.196198Z","title":"arXiv preprint arXiv:2305.18465 , year=","venue":"cs.LG","work_id":"d31ee97b-a474-4e59-928a-ae74b795e83b","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"cited_paper":"/paper/2305.18465","citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:b6641debf7ca712e49ad04cee6b07ef34a4980ae2141de5e2343a156d231c230","observation_id":"2c095830-0150-4fa5-8775-1161d3f5f2da","resolution":{"observed_at":"2026-07-09T22:36:36.197635Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.591099Z","title":null,"venue":null,"work_id":"da1d8fa6-1e41-4d5c-a8de-e8eb4a998568","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:d808bd70c2b3c24f284a5f0f142dd47003e565b51b9cc31af0c3595d9b39c13a","observation_id":"47feac8a-322a-4bc6-bd44-bb4b189bf19f","resolution":{"observed_at":"2026-07-09T22:36:36.592263Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.598570Z","title":"C., and Tao, D.Heterogeneous federated learning: State-of-the-art and research challenges.ACM Computing Surveys 56, 3 (2023), 1–44","venue":null,"work_id":"fc31ce35-740b-4fe0-9381-f41838f0b2be","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:a685667e6224347c938dce396e77f10c69ff8c458fd70e44378035db2a175ea7","observation_id":"3befaf04-a902-4040-ad32-d1a46bb1e0bc","resolution":{"observed_at":"2026-07-09T22:36:36.599930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.577810Z","title":"In International conference on machine learning(2019), PMLR, pp","venue":null,"work_id":"368f8a2d-d2ae-4d1d-90f3-41d6090efc54","year":2019},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:15c95b5704c6bac5a5e0c933bb5c464e3cf4c29a18f7fe97f00b0a185d4a1183","observation_id":"7d4f683c-7a59-48f7-bd8a-ad8ba8c44dc2","resolution":{"observed_at":"2026-07-09T22:36:36.579490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.580019Z","title":"InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2023), pp","venue":null,"work_id":"e3bfb65a-0031-49ac-be34-2968a2addc8d","year":2023},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:1a4bebf2c041d49bd7963995248627b3095c72bd2034c4e796ec99401710bf29","observation_id":"85e9bab8-0f67-49db-bb55-c9ea0faee112","resolution":{"observed_at":"2026-07-09T22:36:36.581586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T22:36:36.582144Z","title":"IEEE Transactions on Parallel and Distributed Systems 33, 12 (2022), 3291–3305","venue":null,"work_id":"ca53ad5c-cc07-498c-9620-6eb99e6bb730","year":2022},"citing_paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-09T22:28:05.992944Z"},"links":{"citing_paper":"/paper/2607.06979"},"observation_digest":"sha256:95e14bcbb84e9055c78c6e184547e86ad3dd4ef0fe80ff3aae0fe20458e9d35f","observation_id":"f1430319-bcdd-4cce-881d-0aa494d30b9c","resolution":{"observed_at":"2026-07-09T22:36:36.583666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.06979","last_updated":"2026-07-08T04:02:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T22:14:30.754967Z","submitted_at":"2026-07-08T04:02:43Z","title":"Robust Federated Learning Under Real-World Client Churn"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":12,"verified_exact":12,"verified_fuzzy":31},"total_outbound_references":57},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2607.06979."}