{"as_of":"2026-08-09T02:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:198dfa832ca72b0e1d9a4e3dfcf7c45085ae93627a6829013ab053baf7bac4fd","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:15:16.516276Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.20871/citation-record","integrity":"/paper/2507.20871/integrity","json":"/paper/2507.20871/citation-record.json","paper":"/paper/2507.20871"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:20.343324Z","title":"6G the next horizon: From connected people and things to connected intelligence,","venue":null,"work_id":"4c8a8137-2020-42e8-b83c-6087c01e0891","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:13.958019Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:c269c3e3396be98a648b519cfd54be528fd54d974628bdb24c96a487926ac0b9","observation_id":"759be0e8-f0a5-4f92-b1f1-0453ed87c94b","resolution":{"observed_at":"2026-08-06T13:15:20.452279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:20.163331Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"6390c1b2-cc1f-42c1-a94f-888bd69e3e45","year":2017},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:13.988824Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:805eb65815e6edc5930f8c31b2a5a811aa93fe4032765ab65453507660e963f8","observation_id":"95271611-8eac-44d1-a127-6ad2510955c5","resolution":{"observed_at":"2026-08-06T13:15:20.238730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:20.012580Z","title":"Advancing federated learning in 6G: a trusted architecture with graph-based analysis,","venue":null,"work_id":"59a0b193-4219-4071-a568-5bf46fd30b39","year":2023},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.018269Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:5527e987e1e45d2520e31e94cf4b0d849d07985a23128b202e0c6b8280bd25da","observation_id":"846ed76d-cff4-4f9c-9dde-5499d05cbd2b","resolution":{"observed_at":"2026-08-06T13:15:20.083373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:19.748818Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"3d19bc40-c1c3-4425-aa1e-affcb5e8fcf5","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.168602Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:f4656f608ea621a0f390a8f15a7fc02743966d0e13dd84ede0d3c059babafa39","observation_id":"e6e3dd91-2210-40bc-b79b-683ff501508e","resolution":{"observed_at":"2026-08-06T13:15:19.889224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:19.549742Z","title":"FLaaS6G: Federated learning as a service in 6G using distributed data management architec- ture,","venue":null,"work_id":"f38cc15d-68ab-4a2a-a1d6-c0d6929507cd","year":2022},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.386083Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:5b21ed7ddf27640f34a0c62777ded5d3a56e579368e5eb9c429aeb038c1796e8","observation_id":"25cfad0a-6743-4b65-aad7-9a4a93b885cf","resolution":{"observed_at":"2026-08-06T13:15:19.641047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:19.381898Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"c6401ec5-9638-4d4c-bbe0-542a88ab2ca2","year":2020},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.574047Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:772525166bf76e3dbde228486dbeeb7d607ad0fbe83d76135a5f909e5a4f56e0","observation_id":"3a487485-754b-4b04-9a98-0c694cbfcfd4","resolution":{"observed_at":"2026-08-06T13:15:19.464486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:19.179333Z","title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,","venue":null,"work_id":"d3ee52f9-bb24-44ad-b66d-55c8445362d7","year":2020},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.662656Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:0dd0b6f1010025a3138dafba88d7c3aa6027f362c620e9f923eab56447722cdf","observation_id":"58bd51c1-6a51-410d-ad2d-6efce3c02b68","resolution":{"observed_at":"2026-08-06T13:15:19.292775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:19.034827Z","title":null,"venue":null,"work_id":"81be6396-7a5a-4afb-982d-92852c4f8f86","year":2022},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.739305Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:d43cb0508c88603d04017dc53c13e653941d6ec842adaa0201a76dcf9bd2e125","observation_id":"d8ae1ce5-f0e9-4467-9242-d8a1eb4a5a5a","resolution":{"observed_at":"2026-08-06T13:15:19.098302Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-07-06T06:42:35.645776Z","submitted_at":"2018-06-02T04:45:58Z","title":"Federated Learning with Non-IID Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00582","snapshot_observed_at":"2026-08-06T13:15:14.898619Z","title":"Federated learning with non-iid data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:14.898619Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:752179d1c6499de912c2015b1920414c06c04b2eee8a741cdcdb6b583b401adf","observation_id":"d204bf65-dab7-4476-a72a-603d592569b5","resolution":{"observed_at":"2026-08-06T13:15:14.898619Z","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":"2026-08-06T13:15:18.849270Z","title":"Towards understanding biased client selection in federated learning,","venue":null,"work_id":"3216d9dd-ff4f-4563-bac6-ee85304c5867","year":2022},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.015706Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:6f4f36a6e969d5633deed48f7f7045884c14c550a04e77f0587d0f04d953c7d0","observation_id":"d6fe463a-f171-4e7a-b2d8-0d2b3cbf2de3","resolution":{"observed_at":"2026-08-06T13:15:18.940295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:18.661847Z","title":"AUCTION: Automated and quality-aware client selection framework for efficient federated learning,","venue":null,"work_id":"20517db7-6d52-4aac-adf8-4748125c84b2","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.122449Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:46596afdcff2aa0a38e2314459be633375e92c0d34e2630782c10908c4d08080","observation_id":"223e982b-529a-4af2-b021-0638973e180c","resolution":{"observed_at":"2026-08-06T13:15:18.761185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:18.505755Z","title":"Client selection for federated learning with heterogeneous resources in mobile edge","venue":null,"work_id":"f23920d3-92c6-40b2-b69b-c34933c18355","year":2019},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.278390Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:a7c0da74c71298fca59ec5bb0fef74d330aa6c6c4e45eff6940bee88e4e96e1e","observation_id":"949d4dff-9144-4e7c-b90e-fc65265bb553","resolution":{"observed_at":"2026-08-06T13:15:18.576377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:18.369000Z","title":"Attention is all you need,","venue":null,"work_id":"87a3693f-c9a3-4884-ab16-c8aa73577f7d","year":2017},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.438166Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:a544c6009f3d89c2d061ecf8436297f0b56c0b299d2c65e3997791d39af9e82d","observation_id":"15ddfd1b-3a3a-4f7a-9544-dffe5d5e2d13","resolution":{"observed_at":"2026-08-06T13:15:18.419189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:18.198335Z","title":"Structured attention networks,","venue":null,"work_id":"d87d18bf-b0ed-417b-9fd1-1cdfa8ec067b","year":2017},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.565436Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:c01b8ba24ba0333cb14580c5b4eed364f5adec7c65e6e724dcc45edbb5cf63bb","observation_id":"a9152e87-7c86-4aa3-96a0-3061e0839a5a","resolution":{"observed_at":"2026-08-06T13:15:18.300011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:18.060407Z","title":"Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective,","venue":null,"work_id":"3220dcc9-2bcf-4ee6-9feb-9bf4579c0e6b","year":2020},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.646970Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:443903ed62281c591aa3724b3d49f695bb934e6a8cdf83c44ed21874dca54822","observation_id":"2675e2fb-78dc-43ed-8a88-f1ff24800a91","resolution":{"observed_at":"2026-08-06T13:15:18.134428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01046","last_updated":"2019-03-22T20:25:57Z","snapshot_observed_at":"2026-07-06T07:30:52.835880Z","submitted_at":"2019-02-04T06:27:41Z","title":"Towards Federated Learning at Scale: System Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01046","snapshot_observed_at":"2026-08-06T13:15:15.757462Z","title":"Towards federated learning at scale: System design,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.757462Z"},"links":{"cited_paper":"/paper/1902.01046","citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:839bf5c4e27b323345f64316bcd34ea46d7820fc94bb38ec5ab7b7214c75a8c8","observation_id":"f72f12f4-aef7-4cb7-b4fc-e198d0ed0530","resolution":{"observed_at":"2026-08-06T13:15:15.757462Z","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":"2026-08-06T13:15:17.811898Z","title":"Federated learning via over- the-air computation,","venue":null,"work_id":"2c7e9172-4cbd-44d8-a060-b7f5409f24da","year":2020},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.833251Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:4d739d09bd0a9af8c567e07a7c6a3909081b0483fee25d05b5c236b70bfd2fda","observation_id":"0644745b-ea58-4ab9-93ea-19556ee23de6","resolution":{"observed_at":"2026-08-06T13:15:17.909782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13408","last_updated":"2025-05-19T17:44:26Z","snapshot_observed_at":"2026-08-07T15:43:00.197948Z","submitted_at":"2025-05-19T17:44:26Z","title":"CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13408","snapshot_observed_at":"2026-08-06T13:15:15.920899Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:15.920899Z"},"links":{"cited_paper":"/paper/2505.13408","citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:4856183e55f3da69c7d811b5eb14693365b82123eb4e2031c9ed49a28ae934d2","observation_id":"7c2133ec-aec7-40b3-8014-8acdcdea1582","resolution":{"observed_at":"2026-08-06T13:15:15.920899Z","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":"2026-08-06T13:15:17.617794Z","title":"ClusterFL: a similarity-aware federated learning system for human activity recogni- tion,","venue":null,"work_id":"a450337a-6aec-49fb-a0e6-fdec9ac9e806","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.014146Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:23654f736b2d31cf1af29f4ccdedf1582cc42240aacc00cac07b5913dcfeecdf","observation_id":"ced8d897-74fe-4ad6-8a34-a1b31e2eebb3","resolution":{"observed_at":"2026-08-06T13:15:17.700500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12641","last_updated":"2019-09-27T12:15:42Z","snapshot_observed_at":"2026-08-02T04:22:20.402569Z","submitted_at":"2019-09-27T12:15:42Z","title":"Active Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12641","snapshot_observed_at":"2026-08-06T13:15:16.088689Z","title":"Active federated learning,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.088689Z"},"links":{"cited_paper":"/paper/1909.12641","citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:c9f2ffacbe8169411d4415607d3608ccec8ff67fb9991acff0a93a0553b31e92","observation_id":"85172bd7-9c10-45fb-ad95-bc967a3fa168","resolution":{"observed_at":"2026-08-06T13:15:16.088689Z","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":"2026-08-06T13:15:17.449320Z","title":"Communication-efficient adaptive federated learning,","venue":null,"work_id":"1e7e6859-b481-4b09-b3ca-f2438761b671","year":2022},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.190471Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:06caec0e368e3e7a66893ef0322a107e54ee237592edc7c0f9b35d38725daa52","observation_id":"4a7b66a3-a7f3-4636-bfcb-4eeadf0aadaf","resolution":{"observed_at":"2026-08-06T13:15:17.539876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:17.269001Z","title":"A review of client selection methods in federated learning,","venue":null,"work_id":"20fa9b51-835c-418c-9e7c-e9bdf3aca53f","year":2024},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.268046Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:3af0ba75941668e3899d7c68e18d3f3d119ccde4bdce1b3fb6f5639528e67ea4","observation_id":"4a523b95-c7d4-4655-87f6-4f2c5a0f666b","resolution":{"observed_at":"2026-08-06T13:15:17.345959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:17.076291Z","title":"Game of gradients: Mitigating irrelevant clients in federated learning,","venue":null,"work_id":"10161a93-5641-40ec-ada3-93ca1626312c","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.351200Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:c7ae9b0d70cb1bf21fa476e855972f4d0468d16984dd2b9288d67c9582cae38c","observation_id":"6792cc7a-7a0f-4178-9eb3-52fd6355c958","resolution":{"observed_at":"2026-08-06T13:15:17.154907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:16.872055Z","title":"Tifl: A tier-based federated learning system,","venue":null,"work_id":"cd935aed-00f1-420c-ad95-a9b2a39829bf","year":2020},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.437387Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:4df62453fb464f9a77bea39cb5a2593be198011d7ed4e788d9357729f7704591","observation_id":"c93cdcc5-2172-4744-be0c-1e198a488e69","resolution":{"observed_at":"2026-08-06T13:15:16.963448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:15:16.699988Z","title":"Client selection based on label quantity information for federated learning,","venue":null,"work_id":"2224cc69-f514-4926-9f83-b2f60196fbb0","year":2021},"citing_paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T13:15:16.516276Z"},"links":{"citing_paper":"/paper/2507.20871"},"observation_digest":"sha256:843758543605c32bbba57e81f474085ffc1e503b77cce77615f6dc12e0bc9580","observation_id":"db28e71a-5aa6-4b46-b492-2b508afcb015","resolution":{"observed_at":"2026-08-06T13:15:16.780342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.20871","last_updated":"2025-08-14T08:57:03Z","latest_version":2,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-07T23:46:27.638693Z","submitted_at":"2025-07-28T14:22:43Z","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.20871."}