{"as_of":"2026-08-16T06:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5fc632a9fccbb51ae3c601b226caf8f73faa866f9f306da898a15629a972773","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":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:00:42.756086Z","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":"2026-05-15T06:55:10.648587Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-14T13:13:45.268194Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05544","last_updated":"2019-08-15T13:53:23Z","snapshot_observed_at":"2026-08-16T01:31:11.805923Z","submitted_at":"2019-08-15T13:53:23Z","title":"On Gossip-based Information Dissemination in Pervasive Recommender Systems","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:13:45.268194Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/1908.05544"},"observation_digest":"sha256:63293a4815f7ec1976b3e75f0ce27f5a4eabaf69c37eb8db3e9776d0d29d6aef","observation_id":"04ccb76e-bd20-4f8e-aa0a-0231578fe094","resolution":{"observed_at":"2026-08-14T13:13:45.268194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-14T13:05:22.216879Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05891","last_updated":"2019-09-01T16:33:58Z","snapshot_observed_at":"2026-08-15T16:16:25.423519Z","submitted_at":"2019-08-16T08:51:27Z","title":"Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:05:22.216879Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/1908.05891"},"observation_digest":"sha256:3b9a609d1cc4eaa4465b8c39c43823e9b2e5445b1be8cf6132cf08a7b252d3e0","observation_id":"93a0bdbc-fb1e-4388-8edf-2cdd1587bc4b","resolution":{"observed_at":"2026-08-14T13:05:22.216879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-14T11:57:36.022735Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.07873","last_updated":"2019-08-21T13:53:23Z","snapshot_observed_at":"2026-08-15T00:39:13.596858Z","submitted_at":"2019-08-21T13:53:23Z","title":"Federated Learning: Challenges, Methods, and Future Directions","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T11:57:36.022735Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/1908.07873"},"observation_digest":"sha256:1f4d580cfc1a591794c998445619830664eea7e59d0a838ccb157f27d4b9b577","observation_id":"490e6733-60e6-4e91-beb0-5b11d2cf5bae","resolution":{"observed_at":"2026-08-14T11:57:36.022735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-12T14:42:06.515270Z","title":"Federated meta-learning with fast convergence and efficient communication","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.15014","last_updated":"2025-07-16T21:34:45Z","snapshot_observed_at":"2026-08-15T23:56:52.314307Z","submitted_at":"2024-11-22T15:42:43Z","title":"On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-12T14:42:06.515270Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2411.15014"},"observation_digest":"sha256:6f9f67059459ad421b2e669c0cf62c0fd63f7cd77a1e4eecb8c996cad681bd77","observation_id":"002eee82-2db3-490e-8239-7f49a69013a9","resolution":{"observed_at":"2026-08-12T14:42:06.515270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-12T04:35:55.302101Z","title":"Federated meta-learning with fast convergence and efficient communication,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.01281","last_updated":"2024-12-02T08:54:43Z","snapshot_observed_at":"2026-08-14T22:58:24.901311Z","submitted_at":"2024-12-02T08:54:43Z","title":"FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T04:35:55.302101Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2412.01281"},"observation_digest":"sha256:348a4f696afcdc968bec4d04907d634b06d413004e1d26e452147caa0063b896","observation_id":"7fbd9f1f-8637-4dd3-9957-8c8d7dfd221f","resolution":{"observed_at":"2026-08-12T04:35:55.302101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-12T04:35:29.479316Z","title":"Federated meta-learning with fast convergence and efficient communication,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.01295","last_updated":"2024-12-02T09:08:51Z","snapshot_observed_at":"2026-08-14T04:20:29.861447Z","submitted_at":"2024-12-02T09:08:51Z","title":"FedAH: Aggregated Head for Personalized Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T04:35:29.479316Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2412.01295"},"observation_digest":"sha256:6568c8e01b53a4706f0dc58fc125cf68c2aafaea3058d9d6947e9f8754680d57","observation_id":"c2c0fa2f-349f-4bac-9658-59593f1c5940","resolution":{"observed_at":"2026-08-12T04:35:29.479316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-11T11:46:08.301468Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15010","last_updated":"2025-09-09T16:03:44Z","snapshot_observed_at":"2026-08-11T11:41:04.305548Z","submitted_at":"2024-12-19T16:22:37Z","title":"Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T11:46:08.301468Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2412.15010"},"observation_digest":"sha256:90ab9e0d4c43379fe4be95399db2c749f02dcc938e888d34b8b5d561e0e45536","observation_id":"7076e7aa-5907-4d9b-a260-bcaa447b3508","resolution":{"observed_at":"2026-08-11T11:46:08.301468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-10T21:57:32.475863Z","title":"Federated meta- learning with fast convergence and efficient communication,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.475863Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:2631eda258042fd2b9e92d4a5f3d49d1819a6a92078ddb5d3cc9fb20a9dd73aa","observation_id":"8c963445-63e5-4d8c-8d52-a654a3b50886","resolution":{"observed_at":"2026-08-10T21:57:32.475863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-16T05:00:42.756086Z","title":"Federated meta-learning with fast convergence and efficient communication,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.21723","last_updated":"2025-05-01T01:47:41Z","snapshot_observed_at":"2026-08-16T04:53:01.437194Z","submitted_at":"2025-04-30T15:09:07Z","title":"Task-Agnostic Semantic Communications Relying on Information Bottleneck and Federated Meta-Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:00:42.756086Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2504.21723"},"observation_digest":"sha256:5ad64f6b8fd6d8c7f30e390527ffe5014caaa0e01b5ce6bf127b5b1bb9d45ac1","observation_id":"9f5fcfde-c3fa-42b5-99e4-5de9770b1cd5","resolution":{"observed_at":"2026-08-16T05:00:42.756086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-05T22:57:18.712993Z","title":"Federated meta-learning with fast convergence and efficient communication","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.06301","last_updated":"2025-08-08T13:24:57Z","snapshot_observed_at":"2026-08-15T18:19:00.125193Z","submitted_at":"2025-08-08T13:24:57Z","title":"FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T22:57:18.712993Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2508.06301"},"observation_digest":"sha256:0e868e8367abc2b94644777b38c34a0f8456d5a418d2d9cff5ff3f3d00ee3532","observation_id":"4bb30bd7-5842-4674-8bc5-baa029fcff43","resolution":{"observed_at":"2026-08-05T22:57:18.712993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-05T16:18:09.987784Z","title":"Federated meta-learning with fast convergence and efficient communication.arXiv:1802.07876, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18774","last_updated":"2025-08-26T07:57:36Z","snapshot_observed_at":"2026-08-12T10:36:32.152921Z","submitted_at":"2025-08-26T07:57:36Z","title":"Federated Learning with Heterogeneous and Private Label Sets","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:09.987784Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2508.18774"},"observation_digest":"sha256:69d66c60574708fd4ed714383581b9b88ad2c8e0aa52baf4ab41ac2265560f49","observation_id":"5de9eafd-9e01-4b44-8705-9c755019e59f","resolution":{"observed_at":"2026-08-05T16:18:09.987784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":"1802.07876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated meta-learning with fast convergence and efficient communication.arXiv preprint arXiv:1802.07876","venue":null,"work_id":"0b24e687-f077-41c0-b238-871304570874","year":2018},"citing_paper":{"arxiv_id":"2605.00892","last_updated":"2026-04-27T20:17:50Z","snapshot_observed_at":"2026-08-13T14:00:37.770464Z","submitted_at":"2026-04-27T20:17:50Z","title":"When To Adapt? Adapting the Model or Data in Federated Medical Imaging","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T20:32:49.268444Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2605.00892"},"observation_digest":"sha256:93dafece2d6f8a28097cb16c2f73101e996c7d39b8353c277a1fdea71e57a114","observation_id":"97ff9de3-b924-4008-b2b2-65437e84c6d4","resolution":{"observed_at":"2026-05-11T15:11:06.028642Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":"1802.07876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated meta-learning with fast convergence and efficient communication.arXiv preprint arXiv:1802.07876","venue":null,"work_id":"0b24e687-f077-41c0-b238-871304570874","year":2018},"citing_paper":{"arxiv_id":"2605.02004","last_updated":"2026-05-14T16:30:30Z","snapshot_observed_at":"2026-08-15T17:38:06.434742Z","submitted_at":"2026-05-03T18:23:30Z","title":"Personalized Digital Health Modeling with Adaptive Support Users","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T19:31:04.654667Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2605.02004"},"observation_digest":"sha256:0ce174b67d2d6035490f7030951cc43044ef5c29f0ff951cbaa57b67ce24e84a","observation_id":"ab169435-f71e-45b4-bafe-271e018fb8d9","resolution":{"observed_at":"2026-05-09T05:50:26.378565Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":"1802.07876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated meta-learning with fast convergence and efficient communication.arXiv preprint arXiv:1802.07876","venue":null,"work_id":"0b24e687-f077-41c0-b238-871304570874","year":2018},"citing_paper":{"arxiv_id":"2605.02004","last_updated":"2026-05-14T16:30:30Z","snapshot_observed_at":"2026-08-15T17:38:06.434742Z","submitted_at":"2026-05-03T18:23:30Z","title":"Personalized Digital Health Modeling with Adaptive Support Users","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T06:52:32.508141Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2605.02004"},"observation_digest":"sha256:3a44a791321a33da4c6ceac31e9df407276822362d505e2283cddc04acf644ba","observation_id":"16264961-b47d-4bd5-877c-705bd78f0a0a","resolution":{"observed_at":"2026-05-15T06:55:10.651952Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-01T18:53:34.726901Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.17169","last_updated":"2026-07-19T10:08:54Z","snapshot_observed_at":"2026-08-12T15:37:00.828137Z","submitted_at":"2026-07-19T10:08:54Z","title":"Joint Channel Estimation and Dynamics-Aware Grouping for Time-Varying RIS-Assisted OTA Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T18:53:34.726901Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2607.17169"},"observation_digest":"sha256:5b335e8c235bcfff89753b16fc061752be63555727d08744448bd9a0c3837a24","observation_id":"f13ddd3e-cdd6-4c28-b4bf-e59a042ef38d","resolution":{"observed_at":"2026-08-01T18:53:34.726901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1802.07876/citation-record","integrity":"/paper/1802.07876/integrity","json":"/paper/1802.07876/citation-record.json","paper":"/paper/1802.07876"},"outbound":[],"paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:1802.07876."}