{"as_of":"2026-08-10T21:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:14cc3cc8082ba858f54b0f8dc385281f2a39cff82953ea2fed6f37b2eb70acc2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T18:54:19.058241Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T13:33:28.488039Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-10T18:54:19.058241Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.10938","last_updated":"2025-01-19T04:20:24Z","snapshot_observed_at":"2026-08-10T18:46:21.107912Z","submitted_at":"2025-01-19T04:20:24Z","title":"Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T18:54:19.058241Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2501.10938"},"observation_digest":"sha256:ace86c6697dbe8db3875c798a2be80670ce3b955c554a5c9ed229e5dc449b381","observation_id":"2e3eef1e-6bd8-49b4-add6-b890f1b7c7b4","resolution":{"observed_at":"2026-08-10T18:54:19.058241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-09T06:02:19.306937Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03092","last_updated":"2025-02-05T11:31:21Z","snapshot_observed_at":"2026-08-09T05:54:20.763649Z","submitted_at":"2025-02-05T11:31:21Z","title":"E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T06:02:19.306937Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2502.03092"},"observation_digest":"sha256:b76aed1129695ba39916f6eeaf87d6eff3d8c1a1be9b7c75e6d55e9b732217b3","observation_id":"1e10963c-02c2-4a66-8a54-6f1245d838d7","resolution":{"observed_at":"2026-08-09T06:02:19.306937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-06T23:56:29.518014Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.15825","last_updated":"2025-06-18T19:09:57Z","snapshot_observed_at":"2026-08-10T09:19:49.752666Z","submitted_at":"2025-06-18T19:09:57Z","title":"Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:56:29.518014Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2506.15825"},"observation_digest":"sha256:a53d8195396d44929c9e41db4ae4dbe519f12897369f7f383c2e79cb21cf55fd","observation_id":"917dc54c-3aa9-43b0-819c-943a25dd2e94","resolution":{"observed_at":"2026-08-06T23:56:29.518014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-06T19:19:11.947862Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.06278","last_updated":"2025-07-08T13:47:40Z","snapshot_observed_at":"2026-08-08T19:15:41.440903Z","submitted_at":"2025-07-08T13:47:40Z","title":"A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-06T19:19:11.947862Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2507.06278"},"observation_digest":"sha256:0a712f5895c178f6d77918d43d08f842c274593dac42513cf4f9233dbc22621f","observation_id":"42fdfb50-c1f4-43a3-a969-c3d808803428","resolution":{"observed_at":"2026-08-06T19:19:11.947862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-06T16:12:20.480072Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14487","last_updated":"2025-07-19T05:06:38Z","snapshot_observed_at":"2026-08-06T15:52:07.248128Z","submitted_at":"2025-07-19T05:06:38Z","title":"Federated Reinforcement Learning in Heterogeneous Environments","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:12:20.480072Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2507.14487"},"observation_digest":"sha256:3478efd8cf3e8ebea846ac5bb4e707d88b0f13562a60a040cc475395406d4670","observation_id":"49c703eb-fb1c-40a6-8848-f03bdff75082","resolution":{"observed_at":"2026-08-06T16:12:20.480072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-08-04T18:09:29.057515Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.057515Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:6a33df7449409ebc508e638d8efb7864aca824560ce5d065b610adfa2cf01de7","observation_id":"e2d22a59-4d3a-4b8e-9b54-c3c34a066e47","resolution":{"observed_at":"2026-08-04T18:09:29.057515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":"2108.11887","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-06-29T13:33:28.488039Z","title":"arXiv preprint arXiv:2108.11887 , year =","venue":null,"work_id":"f13c5e14-8225-4229-b2e0-9e6e5abfd8ff","year":2021},"citing_paper":{"arxiv_id":"2604.05088","last_updated":"2026-04-06T18:42:31Z","snapshot_observed_at":"2026-08-04T01:46:38.262779Z","submitted_at":"2026-04-06T18:42:31Z","title":"Scalar Federated Learning for Linear Quadratic Regulator","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T19:03:53.389020Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2604.05088"},"observation_digest":"sha256:7266671672b93d3551d85dd81396eebaea18728cf2fe39c3eeedf0021e7f47b0","observation_id":"34c1ef7d-dc31-4b06-aff2-448dcfcd4287","resolution":{"observed_at":"2026-05-10T23:30:52.429777Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":"2108.11887","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-06-29T13:33:28.488039Z","title":"arXiv preprint arXiv:2108.11887 , year =","venue":null,"work_id":"f13c5e14-8225-4229-b2e0-9e6e5abfd8ff","year":2021},"citing_paper":{"arxiv_id":"2605.02165","last_updated":"2026-05-04T02:46:09Z","snapshot_observed_at":"2026-08-07T00:27:08.964122Z","submitted_at":"2026-05-04T02:46:09Z","title":"Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T19:15:49.922731Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2605.02165"},"observation_digest":"sha256:afaba64a2fe76f47b3bf211211d9a4315c93214a4061f977de9999d3c9b57b98","observation_id":"80194a04-08b0-4e0e-bd16-20a513348340","resolution":{"observed_at":"2026-05-09T06:00:34.797514Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":"2108.11887","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-06-29T13:33:28.488039Z","title":"arXiv preprint arXiv:2108.11887 , year =","venue":null,"work_id":"f13c5e14-8225-4229-b2e0-9e6e5abfd8ff","year":2021},"citing_paper":{"arxiv_id":"2605.08268","last_updated":"2026-05-08T03:10:22Z","snapshot_observed_at":"2026-07-06T23:20:33.816373Z","submitted_at":"2026-05-08T03:10:22Z","title":"Insider Attacks in Multi-Agent LLM Consensus Systems","version":1},"reference_index":223,"source":"arxiv_source","source_observed_at":"2026-05-12T00:52:23.019201Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2605.08268"},"observation_digest":"sha256:0f954300abcb5716ef285c815ac28733007f6e3d2298baee50509b7aa868474e","observation_id":"9baef010-f8dd-4335-b67a-7b92e5dff269","resolution":{"observed_at":"2026-05-12T08:41:23.775900Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":"2108.11887","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-06-29T13:33:28.488039Z","title":"arXiv preprint arXiv:2108.11887 , year =","venue":null,"work_id":"f13c5e14-8225-4229-b2e0-9e6e5abfd8ff","year":2021},"citing_paper":{"arxiv_id":"2605.08378","last_updated":"2026-05-08T18:36:25Z","snapshot_observed_at":"2026-08-02T18:04:56.183678Z","submitted_at":"2026-05-08T18:36:25Z","title":"Reinforcement Learning for Scalable and Trustworthy Intelligent Systems","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-12T01:47:40.772146Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2605.08378"},"observation_digest":"sha256:15d9ef1e292ca325a8590de7feda10068a16b0201c1dbf60b2d5ebff5885adfe","observation_id":"35984cec-39d0-4890-b1f6-3730166cda9f","resolution":{"observed_at":"2026-05-12T07:51:40.758041Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","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":"2026-07-12T23:02:52.079919Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.27385","last_updated":"2026-04-10T19:37:16Z","snapshot_observed_at":"2026-08-09T21:37:18.248833Z","submitted_at":"2026-04-10T19:37:16Z","title":"Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-12T23:02:52.079919Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2605.27385"},"observation_digest":"sha256:bfa81a9958a2ea6317e6a7bb7fdfa4b82fe9704d9223cf194ed8d3239119cd2a","observation_id":"cd38a438-7177-48a5-a1c2-fc7c033abc6f","resolution":{"observed_at":"2026-07-12T23:02:52.079919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":"2108.11887","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-06-29T13:33:28.488039Z","title":"arXiv preprint arXiv:2108.11887 , year =","venue":null,"work_id":"f13c5e14-8225-4229-b2e0-9e6e5abfd8ff","year":2021},"citing_paper":{"arxiv_id":"2605.29002","last_updated":"2026-05-27T18:59:40Z","snapshot_observed_at":"2026-08-05T22:42:24.844725Z","submitted_at":"2026-05-27T18:59:40Z","title":"FedQHD: Closed-Form Function-Space Federated Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T13:24:31.382577Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2605.29002"},"observation_digest":"sha256:b5ddcbf7c17d38f8f9951f330a6f290c3ab2b1d96bd34c201f9afd3e9013b4ad","observation_id":"768d434e-6465-4261-a776-f14d289996aa","resolution":{"observed_at":"2026-06-29T13:33:28.489624Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2108.11887/citation-record","integrity":"/paper/2108.11887/integrity","json":"/paper/2108.11887/citation-record.json","paper":"/paper/2108.11887"},"outbound":[],"paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2108.11887."}