{"as_of":"2026-08-17T17:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38bde1e4f5dc294bfc18d8679c5fbdf8aa7ab2eac038cb8b3a68b1b3f1151d03","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:24:27.492445Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.09959/citation-record","integrity":"/paper/2505.09959/integrity","json":"/paper/2505.09959/citation-record.json","paper":"/paper/2505.09959"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:24:27.837079Z","title":"Deep learning with differential privacy","venue":null,"work_id":"44e36ce4-1208-47dc-9ea0-69dfcfaaf529","year":2016},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.249802Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:59a96c3e45089e450bb788ab91d561c6ac7fca4b504b2ddddfa3a99731a5a643","observation_id":"fb793582-7c90-44c6-ba4b-cefc840b4989","resolution":{"observed_at":"2026-08-15T21:24:27.840092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.828619Z","title":"Mico: Im- proved representations via sampling-based state similarity for markov decision processes.Advances in Neural Infor- mation Processing Systems, 34:30113–30126,","venue":null,"work_id":"53781c50-d318-4b35-a93b-c7beb5d55800","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.388448Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:b6159122146bfb8f0fdb6dae53766e67f4bc7c323a2dd84ba16907a3a7e0d32a","observation_id":"b2c88f85-0f19-4a41-b6bc-0064c6acbd28","resolution":{"observed_at":"2026-08-15T21:24:27.831690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.820109Z","title":"Scalable methods for computing state similarity in deterministic markov deci- sion processes","venue":null,"work_id":"56b21781-d77a-4c20-845c-7fb9a50eb63d","year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.391301Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:2ef4bfb83d0655e167a39f2dff0289bf35d653f675f53fe0de20e89311f9b237","observation_id":"925ec993-e123-487c-b484-97353381e7d4","resolution":{"observed_at":"2026-08-15T21:24:27.823189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.793977Z","title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach.Advances in neural information processing sys- tems, 33:3557–3568,","venue":null,"work_id":"142ba391-b651-43b5-9478-792fa3688af3","year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.400092Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:7cf9b4fedc979e8245b1d98c73f77f649fb23520dee7c71b9392052a853e9f08","observation_id":"3404f9bc-0019-4157-aed4-6e844df8d70b","resolution":{"observed_at":"2026-08-15T21:24:27.797318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.785631Z","title":"Fault-tolerant federated reinforcement learning with the- oretical guarantee.Advances in Neural Information Pro- cessing Systems, 34:1007–1021,","venue":null,"work_id":"251f6339-2774-412d-bd67-7c8ad6b3634b","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.403744Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:f9be0d892b0b1061a90cee0cfc53ba1094ac08c64a9cc75569e1441687b71e5e","observation_id":"85eccdc6-10df-49fe-b11d-e355a12f58c9","resolution":{"observed_at":"2026-08-15T21:24:27.788768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11135","last_updated":"2023-01-26T14:39:34Z","snapshot_observed_at":"2026-08-16T16:46:51.691828Z","submitted_at":"2023-01-26T14:39:34Z","title":"FedHQL: Federated Heterogeneous Q-Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.11135","snapshot_observed_at":"2026-08-15T21:24:27.408187Z","title":"Fedhql: Federated heterogeneous q-learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.408187Z"},"links":{"cited_paper":"/paper/2301.11135","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:aed05e0791871908044489f20a579ef9a0b9cbdb19f579114ff11d3a669d927e","observation_id":"a52d40f5-14c9-4177-b11c-bfc75cee8221","resolution":{"observed_at":"2026-08-15T21:24:27.408187Z","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":"2026-08-15T21:24:27.768089Z","title":"Bisimulation metrics for continuous markov decision processes.SIAM Journal on Computing, 40(6):1662–1714,","venue":null,"work_id":"d99f860c-e75c-4f8c-924f-6dc82672867b","year":2011},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.415332Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:4913598d4c74a2cb9179b7d1ce89d01eae888ee88112cd2560d8f5f6375cd136","observation_id":"61bb0f1f-f0ad-45d0-8dd7-1fec51f3060c","resolution":{"observed_at":"2026-08-15T21:24:27.771238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.759443Z","title":"Federated reinforcement learn- ing with environment heterogeneity","venue":null,"work_id":"2b47e5c1-1160-4faa-a593-d2f9f1a23716","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.427020Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:155e79d6ff2f8e54364654ce125f515c8fbcead369a4146e46f071ae39e2d010","observation_id":"f7cff97d-8b57-40f8-abed-4ee21c711b72","resolution":{"observed_at":"2026-08-15T21:24:27.762604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.750632Z","title":"Towards robust bisimulation metric learning.Advances in Neural Infor- mation Processing Systems, 34:4764–4777,","venue":null,"work_id":"ec5d4034-ce20-4a61-8ea4-8168cfabe817","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.430054Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:f34594ed1a18b439597af7db5e0a5660ca3aa3536a7af7968b8d1cb84b9b4500","observation_id":"595fc78b-51ef-4dfa-ad86-edbccd637b15","resolution":{"observed_at":"2026-08-15T21:24:27.753853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.01523","last_updated":"2020-07-14T08:12:35Z","snapshot_observed_at":"2026-08-14T23:16:03.815025Z","submitted_at":"2020-01-06T12:40:21Z","title":"Think Locally, Act Globally: Federated Learning with Local and Global Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.01523","snapshot_observed_at":"2026-08-15T21:24:27.432753Z","title":"Think locally, act globally: Federated learning with local and global representations.arXiv preprint arXiv:2001.01523,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.432753Z"},"links":{"cited_paper":"/paper/2001.01523","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:d559142b216111d6e938d289a82b7e0a0802c5814cd56108bbef72a4631e8c0f","observation_id":"e09e6fdc-cb81-4da3-b975-76e750144b93","resolution":{"observed_at":"2026-08-15T21:24:27.432753Z","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":"2026-08-15T21:24:27.741815Z","title":"Policy-independent behavioral metric-based rep- resentation for deep reinforcement learning.Proceed- ings of the AAAI Conference on Artificial Intelligence, 37:8746–8754, 06","venue":null,"work_id":"18af9e3f-e6da-406c-969a-1a1076e153fc","year":2023},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.435514Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:781bdb3af95282256e11d6eafb7cda5fc4067654018d5dabfaf0b94c9184e58a","observation_id":"fc61d14e-c245-4cfb-bd73-3d0b1e896e92","resolution":{"observed_at":"2026-08-15T21:24:27.745193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.732676Z","title":"Threats to federated learning: A survey,","venue":null,"work_id":"05db0f5e-5203-4e95-8a12-667f1d51b4db","year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.437883Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:b1bf6e46de45abb93ac4dad8061b5e72b74cef1d8a622a150c5754526fab5713","observation_id":"8d4e8b65-877a-4741-81b1-cd4efd3e7712","resolution":{"observed_at":"2026-08-15T21:24:27.736147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:24:27.440142Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.440142Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:e4160d5830056cbdc1eb0995e39df6b801be74ea522bed5da7e7f53f1c0dcae1","observation_id":"d8d3fea5-e89f-4063-9473-94179845fbaa","resolution":{"observed_at":"2026-08-15T21:24:27.440142Z","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":"2026-08-15T21:24:27.710481Z","title":"A survey on security and privacy of federated learning.Future Generation Computer Systems, 115:619–640,","venue":null,"work_id":"f8d70d90-1eed-481b-81ae-eab7122e916f","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.445068Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:de93235efe91cc1780961bfef67047159ec4a927ab65a3fc73c47b133f22a578","observation_id":"fc443d73-0799-4746-be1e-0105d8490d17","resolution":{"observed_at":"2026-08-15T21:24:27.713626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.701601Z","title":"Privacy-preserving federated learning using homomorphic encryption.Applied Sciences, 12(2):734,","venue":null,"work_id":"14113c7a-7d9f-463f-bdab-becf0de7d203","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.447228Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:4dcfac2cce08be1113da737649b674dbd89e83b9c8cc8c09ddb2963e1c4dbc9c","observation_id":"3f50439a-5ed8-48d1-8a2d-4940325482df","resolution":{"observed_at":"2026-08-15T21:24:27.704771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-08-16T18:00:44.288700Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-08-15T21:24:27.449205Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges.arXiv preprint arXiv:2108.11887,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.449205Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:75c1a2eee4ff6ca698f4cbc7b9acd4e2488a4f1189b9ed0c33e51c3bd0b0b43c","observation_id":"6363fb45-ca1f-463a-8dda-88b4ae2923f4","resolution":{"observed_at":"2026-08-15T21:24:27.449205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00295","last_updated":"2021-09-08T23:37:17Z","snapshot_observed_at":"2026-08-13T06:06:10.844945Z","submitted_at":"2020-02-29T16:37:29Z","title":"Adaptive Federated Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.00295","snapshot_observed_at":"2026-08-15T21:24:27.451522Z","title":"Adaptive federated optimization.arXiv preprint arXiv:2003.00295,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.451522Z"},"links":{"cited_paper":"/paper/2003.00295","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:c09def4d453a218d6132b41a3c068cc793f43eef2c2e44b0f42b187475f86fab","observation_id":"0240c8f0-44b7-488a-91d9-b241c909d240","resolution":{"observed_at":"2026-08-15T21:24:27.451522Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:24:27.453799Z","title":"Personalized federated learning with moreau en- velopes.Advances in neural information processing sys- tems, 33:21394–21405,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.453799Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:9e74ec0588c2bb5712b7de58176399d46bf99c08d6a4d00f43fe0cfd65c2a82d","observation_id":"3d56bbf8-f5e4-4a1c-a511-7f5a4c4f6e22","resolution":{"observed_at":"2026-08-15T21:24:27.453799Z","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":"2026-08-15T21:24:27.687895Z","title":"Federated learning from pre-trained models: A contrastive learning ap- proach.Advances in neural information processing sys- tems, 35:19332–19344,","venue":null,"work_id":"8a69affc-5411-4d40-bbcc-a749ce97aa12","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.456141Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:565f7f14a0b960dbf0e5da5b01c3c5b9e3e50a479b6155da7e12d5787243c4d6","observation_id":"7e2aa028-0c49-4a01-bdeb-fcf866c17888","resolution":{"observed_at":"2026-08-15T21:24:27.691228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.02075","last_updated":"2024-07-18T01:18:10Z","snapshot_observed_at":"2026-08-16T16:11:03.124914Z","submitted_at":"2022-12-05T07:40:29Z","title":"Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks","version":4},"cited_work":{"arxiv_id":"2212.02075","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.02075","snapshot_observed_at":"2026-08-15T21:24:27.549969Z","title":"Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks","venue":"cs.NI","work_id":"9c3c6db7-e5ee-479e-b681-54ff10205897","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.459405Z"},"links":{"cited_paper":"/paper/2212.02075","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:4eec15137ba9103917d4c59d37d2fa2ddff0c6d62d4e6ef75e00f879edca056d","observation_id":"28d3b84a-01e5-4c4c-83cf-59ae7ac7035f","resolution":{"observed_at":"2026-08-15T21:24:27.556140Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-15T21:24:27.462645Z","title":"Deepmind control suite.arXiv preprint arXiv:1801.00690,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.462645Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:dcc1d95addc4c6c7a5b0763380d8fb4f6c1a3ab891f0d8254b1873f9e95dae46","observation_id":"f4924f5c-39ff-4c37-8aa1-a33671b683b7","resolution":{"observed_at":"2026-08-15T21:24:27.462645Z","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":"2026-08-15T21:24:27.679973Z","title":"A hybrid approach to privacy-preserving feder- ated learning","venue":null,"work_id":"c4fd4617-5740-4913-8dc1-edc92c29189f","year":2019},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.466016Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:7bcee131919ca97e41d677fd617195052439ad8077ccf9c1ef4cae90f6a46253","observation_id":"e514469a-9efe-431a-9042-ce1815ef656c","resolution":{"observed_at":"2026-08-15T21:24:27.682710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.663405Z","title":"Turbosvm-fl: Boost- ing federated learning through svm aggregation for lazy clients","venue":null,"work_id":"3f5d56fa-4f71-4baa-8270-057cf5c29089","year":2024},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.471964Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:b26bbc9b5a2cdbbc100044fe5cf03c26e7c9912ed007f8fa16890ff9c7169209","observation_id":"3a99dc7e-4a42-42e8-985d-d868e6b6c761","resolution":{"observed_at":"2026-08-15T21:24:27.666299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10742","last_updated":"2021-04-07T01:57:14Z","snapshot_observed_at":"2026-08-12T08:19:24.097653Z","submitted_at":"2020-06-18T17:59:35Z","title":"Learning Invariant Representations for Reinforcement Learning without Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10742","snapshot_observed_at":"2026-08-15T21:24:27.477890Z","title":"Learn- ing invariant representations for reinforcement learning without reconstruction.CoRR, abs/2006.10742,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.477890Z"},"links":{"cited_paper":"/paper/2006.10742","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:dcf5777ed8205a8c24c36484976dced2639d0e65f318019b7ce34a3c2523668e","observation_id":"49aed078-ddc7-42b7-9de5-14a9594c55af","resolution":{"observed_at":"2026-08-15T21:24:27.477890Z","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":"2026-08-15T21:24:27.646994Z","title":"No free lunch theorem for se- curity and utility in federated learning.ACM Transactions on Intelligent Systems and Technology, 14(1):1–35,","venue":null,"work_id":"fc58c308-9f1c-4eb6-956c-9fac677f0221","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.480890Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:f2c6a71e3cb67fa28b30da938d539498ec3d2ade496a91bbe20425ce32c894df","observation_id":"40d31800-c655-4806-b852-e16465c0b531","resolution":{"observed_at":"2026-08-15T21:24:27.649910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.638707Z","title":"Federated unsupervised representation learning.Frontiers of Information Technol- ogy & Electronic Engineering, 24(8):1181–1193,","venue":null,"work_id":"323397e2-c263-4169-87ae-2d66be7a5434","year":2023},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.483650Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:39244036d3e41224bbbe79b2c110e49098424db8343e79ef0c5ff6ca170dedfb","observation_id":"6e8c9a09-d682-4127-8315-8e8fc9c22d42","resolution":{"observed_at":"2026-08-15T21:24:27.641483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-08-15T04:44:38.370440Z","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":"2026-08-15T21:24:27.486600Z","title":"Fed- erated learning with non-iid data.arXiv preprint arXiv:1806.00582,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.486600Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:fa0245dcbcc929a577e15d7925f9699ec157f057104342238934c286ec7cdc0c","observation_id":"548f71d0-aef0-4f75-836c-d356227e7da8","resolution":{"observed_at":"2026-08-15T21:24:27.486600Z","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":"2026-08-15T21:24:27.628996Z","title":"Deep leakage from gradients,","venue":null,"work_id":"dd6632b2-e848-4f0a-bde1-28709644b99e","year":2019},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.489942Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:bb6f86e34154743b4ee4f1288b7ff4d744feeda228222086efa5974213d283ad","observation_id":"82467fec-e257-4f6d-a384-8e9565ed8702","resolution":{"observed_at":"2026-08-15T21:24:27.632880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.08277","last_updated":"2020-02-09T12:30:36Z","snapshot_observed_at":"2026-08-16T07:04:05.308711Z","submitted_at":"2019-01-24T08:25:29Z","title":"Federated Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.08277","snapshot_observed_at":"2026-08-15T21:24:27.492445Z","title":"Federated deep reinforcement learning.arXiv preprint arXiv:1901.08277,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.492445Z"},"links":{"cited_paper":"/paper/1901.08277","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:9bc108238ac08a22e476a971009f1808e762d164ed686c749255ecabdb18ae58","observation_id":"d07fc6b2-b736-4bc4-8022-dbb74da43b10","resolution":{"observed_at":"2026-08-15T21:24:27.492445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.07557","last_updated":"2018-03-01T10:12:27Z","snapshot_observed_at":"2026-08-16T10:57:46.215293Z","submitted_at":"2017-12-20T16:28:37Z","title":"Differentially Private Federated Learning: A Client Level Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.07557","snapshot_observed_at":"2026-08-15T21:24:27.418740Z","title":"Differentially private federated learning: A client level perspective.arXiv preprint arXiv:1712.07557,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.418740Z"},"links":{"cited_paper":"/paper/1712.07557","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:07d0eb366b87c4b6f6b46dae99a2f248d949f16a1b59f551ab3bbff7d8ee089f","observation_id":"8dd7e283-a87c-4e6b-abb7-3e75c8e75c55","resolution":{"observed_at":"2026-08-15T21:24:27.418740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.05265","last_updated":"2021-03-18T13:58:01Z","snapshot_observed_at":"2026-08-16T18:52:29.068861Z","submitted_at":"2021-01-13T18:55:43Z","title":"Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.05265","snapshot_observed_at":"2026-08-15T21:24:27.263005Z","title":"Contrastive behavioral similarity embeddings for generalization in reinforcement learning.arXiv preprint arXiv:2101.05265,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.263005Z"},"links":{"cited_paper":"/paper/2101.05265","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:127bd375ba7b08bbe2712429509d9e2f4aade441a40968a44d1f601ac1b9476d","observation_id":"2667947a-954b-4298-82c3-e299e98c758e","resolution":{"observed_at":"2026-08-15T21:24:27.263005Z","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":"2026-08-15T21:24:27.719382Z","title":"Aby3: A mixed protocol framework for machine learning","venue":null,"work_id":"722c69b2-faa9-405f-ad23-328d94c424aa","year":2018},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.442600Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:9622b9669d5083a226050a76bd12880e40e7cb73e8eb52a1b7c8972ab343ec63","observation_id":"c6c2b979-84c2-4440-ad01-d087f7ae4739","resolution":{"observed_at":"2026-08-15T21:24:27.722332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-15T21:24:27.423280Z","title":"Soft actor-critic algorithms and applications","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.423280Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:4f3c8c2c5fdc920e15d94f268ad6ad32a296dda89fdf7c98589545ae8dac0d94","observation_id":"4e00b73f-371a-43c5-be00-5ddc7ae020f4","resolution":{"observed_at":"2026-08-15T21:24:27.423280Z","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":"2026-08-15T21:24:27.671711Z","title":"Optimizing federated learning on non- iid data with reinforcement learning","venue":null,"work_id":"53b97c8a-768b-49bc-be1e-964829a25464","year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.469003Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:04502fd2564a417250d3c745c3096ac5ac7fa2447c9157a9b89aff0c89f6eed6","observation_id":"a395b782-7eb5-44d9-94d9-f1406e680ebf","resolution":{"observed_at":"2026-08-15T21:24:27.674797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.811593Z","title":"Learn- ing representations via a robust behavioral metric for deep reinforcement learning.Advances in Neural Information Processing Systems, 35:36654–36666,","venue":null,"work_id":"00108ef1-4f60-4cdf-b88c-b0ecbd540673","year":2022},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.394185Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:1d6d15ded6e35253f4dd8f06d38002076e9e47df7d61f23fd60ce46970d7c61b","observation_id":"e08d189d-c095-428a-8ef2-f5e839400c8b","resolution":{"observed_at":"2026-08-15T21:24:27.814769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06473","last_updated":"2021-03-11T05:39:52Z","snapshot_observed_at":"2026-08-16T18:39:43.417910Z","submitted_at":"2021-03-11T05:39:52Z","title":"Multi-Task Federated Reinforcement Learning with Adversaries","version":1},"cited_work":{"arxiv_id":"2103.06473","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.06473","snapshot_observed_at":"2026-08-15T21:24:27.611867Z","title":"Multi-Task Federated Reinforcement Learning with Adversaries","venue":"cs.LG","work_id":"453b5902-3a3d-41eb-95e2-bab73841b8bf","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.334512Z"},"links":{"cited_paper":"/paper/2103.06473","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:11e1ce5d49d20fe72fa8d8748782986512bad8f822ef4c6cb17df919c7051616","observation_id":"c9604ca5-4444-4e33-a118-5baa0849c3f3","resolution":{"observed_at":"2026-08-15T21:24:27.615399Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.802976Z","title":"Exploiting shared repre- sentations for personalized federated learning","venue":null,"work_id":"ec663e8b-6e4f-43e4-b2bd-5d153733f3df","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.396978Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:003e6d269bc3cdf5807570e9613b18aaaf2346af2a9cd4aebd3c01736747360f","observation_id":"9f8ebf99-d166-4723-80a9-cdb99ac1be64","resolution":{"observed_at":"2026-08-15T21:24:27.806141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.776607Z","title":"Pri- vacy preserving machine learning with homomorphic en- cryption and federated learning.Future Internet, 13(4):94,","venue":null,"work_id":"e5041889-49e6-4551-b9f9-9356bb13b0be","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.412092Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:88268ab76af55c13ec7ac3b70fc1adcddcaa1b6d042810d4a7313cfc49340dd1","observation_id":"1a567423-fbab-4c77-bb77-02f17500b032","resolution":{"observed_at":"2026-08-15T21:24:27.779793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:24:27.655433Z","title":"Federated learning with dif- ferential privacy: Algorithms and performance analysis","venue":null,"work_id":"17d190d2-48c3-4dbb-83ac-6cc9ad733027","year":2020},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.474940Z"},"links":{"citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:47eee80a788d9beafd4656200cc0d4ae69c1625997db782186e583ee731958d5","observation_id":"4b6a98b9-3101-4ed6-a561-a86f56d9efba","resolution":{"observed_at":"2026-08-15T21:24:27.658206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":2,"verified_fuzzy":24},"total_outbound_references":39},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.09959."}