{"as_of":"2026-08-10T10:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6588876d86442e399c5e29ce8a296c6ab5f94cfc8d0b0bde10f317f32c93f44d","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T22:38:37.091481Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T14:20:21.472989Z","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":"2026-05-10T14:20:29.463175Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"cited_work":{"arxiv_id":"2502.09104","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.09104","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"One-shot federated learning methods: A practical guide","venue":null,"work_id":"33dd5fc3-7e2c-4b04-840f-1c1dcab76abd","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2502.09104","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:992ea85b9e130b3f5833abfcd4ad25cd38fd03ceb4c0af1c6835c16a64822098","observation_id":"86343c57-7e41-418c-9ceb-f5538e11fea9","resolution":{"observed_at":"2026-05-10T14:20:29.465694Z","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/2502.09104/citation-record","integrity":"/paper/2502.09104/integrity","json":"/paper/2502.09104/citation-record.json","paper":"/paper/2502.09104"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:39.177982Z","title":"Rfclock: Timing, phase and frequency synchroniza- tion for distributed wireless networks","venue":null,"work_id":"9c70a8e5-08cf-4869-b361-453e8686987b","year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.370968Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:9880b1922965e2720c2688731fdbc3960fe60b70b559f3afb7d7df3effde7338","observation_id":"01ca1550-226d-4993-94e5-5d227834650b","resolution":{"observed_at":"2026-08-07T22:38:39.182129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:39.064299Z","title":"Practical secure aggregation for privacy-preservin g machine learning","venue":null,"work_id":"a85de766-9376-4475-abe1-7a181ed73dbb","year":2017},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.436918Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:bf478a03a0e82ccce49d32ab15fd3613157c72c0ea92fbe8bbabdf56dee1fe3b","observation_id":"6ea1261c-74a5-4c7b-9a2d-a50b508ac4ad","resolution":{"observed_at":"2026-08-07T22:38:39.094412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.921199Z","title":"Homomorphic encryption for arithmetic of approximate numbers","venue":null,"work_id":"a09d6c54-24c5-438a-961c-e872e228fa5e","year":2017},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.474714Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f003bcce25b4a5048822899492f1d15530ea397daed380458eebebfd131a6746","observation_id":"d2c363ec-1e9f-4257-9c21-f0ac67bea05b","resolution":{"observed_at":"2026-08-07T22:38:38.972442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.803281Z","title":"Enhancing one- shot federated learning through data and ensemble co- boosting","venue":null,"work_id":"f69b07e4-917e-445c-962e-2abc109f75e0","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.480226Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:46ea24e0bfbe5a9ee946d1ed0469b14eee94ff32f2ce1dade8ea45a650db2f51","observation_id":"eaa00b40-319e-4323-99af-dce9106270ab","resolution":{"observed_at":"2026-08-07T22:38:38.863556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.760909Z","title":"Heterogeneity for the win: One-shot feder- ated clustering","venue":null,"work_id":"da7f20ba-b5b7-4d7a-9655-5cd9c002ca69","year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.484885Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:916a6783db25d97141ffd54f263ea8617b39cd446b623e3c4ac85680b140a3fa","observation_id":"f5e21d58-b8a8-41bb-b5dc-f2c7d8ea733d","resolution":{"observed_at":"2026-08-07T22:38:38.765383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.746213Z","title":"Heterofl: Computation and communication efficient feder- ated learning for heterogeneous clients","venue":null,"work_id":"432e0774-7936-4dc4-8e13-1c72d374402e","year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.489424Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:c647c6a8916f497de9763674a30eb858ab8670c68740d88ce7055e79096b5ff9","observation_id":"84b597ac-86a3-4147-8218-a73a045ab5b4","resolution":{"observed_at":"2026-08-07T22:38:38.751162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.732270Z","title":"Towards addressing label skews in one-shot federated learning","venue":null,"work_id":"a64c45de-ae3a-4201-8003-aef4065e38f5","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.493885Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:a8014b7468c9a814d96b9d3ce64c80f4e63c2e9a42795feb8393d11f78139903","observation_id":"00e26ab1-d0cc-46ff-9c32-5fd67c13d03b","resolution":{"observed_at":"2026-08-07T22:38:38.736866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1902.11175","last_updated":"2019-03-05T20:33:39Z","snapshot_observed_at":"2026-08-06T14:20:09.815961Z","submitted_at":"2019-02-28T15:55:18Z","title":"One-Shot Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.11175","snapshot_observed_at":"2026-08-07T22:38:36.511834Z","title":"One-shot federated learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.511834Z"},"links":{"cited_paper":"/paper/1902.11175","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f30b2e61844725e1835d633063066c233771758c22590b3dfc26e5f8dc3bceeb","observation_id":"f12d457b-7af0-4d5c-9404-8af7afbfb586","resolution":{"observed_at":"2026-08-07T22:38:36.511834Z","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":"2026-08-07T22:38:38.646660Z","title":"Data-free one-shot federated learning un - der very high statistical heterogeneity","venue":null,"work_id":"c33dc4cc-4b16-4d2f-b7be-cefbfcff4217","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.524088Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:420ae88689e608b3097e9acf391345740095ff0e44b6deaecc2e03dc6d274b58","observation_id":"ff9fc819-4a0b-4685-ae3c-d98b4a8832aa","resolution":{"observed_at":"2026-08-07T22:38:38.651799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.632291Z","title":"Practical hybrid gradient compression for federated learn - ing systems","venue":null,"work_id":"cacf4c92-6b1c-4032-a1f5-4da659b03746","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.527969Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:5ea7e7973dc8795571e92a2e541c573d7173684dd7f4470007f57f1d7b931c41","observation_id":"0719c6b8-a7ea-48f0-9a55-10fa58fd7bb1","resolution":{"observed_at":"2026-08-07T22:38:38.637129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.618283Z","title":"One-shot federated conformal prediction","venue":null,"work_id":"c666cbdf-c7d4-4287-9210-e2a60f3ac6f1","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.532177Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f58c174755ca2aaf121f7cb0d5041e43c41954e73b2eb05f1fde0a5570aa239b","observation_id":"aa740b4d-c149-45fb-b156-e16d7a063623","resolution":{"observed_at":"2026-08-07T22:38:38.622877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.575701Z","title":"Adaptive mix- tures of local experts","venue":null,"work_id":"12ef9f3f-94c9-4222-9edd-09fcff6a85f7","year":1991},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.536420Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:669414c3fb4186573dad83c4fc4b0d272d7d0647caf3f8dd522a86c43d3916f2","observation_id":"350a2735-c6fb-4892-beba-86b9646b640b","resolution":{"observed_at":"2026-08-07T22:38:38.608167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.408933Z","title":"Ofl-w3: A one-shot federated learning system on web 3.0","venue":null,"work_id":"fbc47e9b-d760-4b81-bad3-e553b678f046","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.545585Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:2a0c14ddb485a0da25fa8a9b605b22aa9701addd8fcef0d8db4c63a12ef0b53a","observation_id":"29898a49-0c27-463d-b0e5-79b9882b078c","resolution":{"observed_at":"2026-08-07T22:38:38.439751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.275547Z","title":"Advances and open problems in federated learning","venue":null,"work_id":"de5303f9-b60c-40ad-98ab-15c473e8e61a","year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.549952Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:bb4e656f3dbe025f5b18d8490f51864109009adf4329465fee080cc2b7e00e90","observation_id":"ca7693b3-6552-4dae-ab34-d508d37ca90d","resolution":{"observed_at":"2026-08-07T22:38:38.337108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.230485Z","title":"One-shot federated learning on medi- cal data using knowledge distillation with image synthesis and client model adaptation","venue":null,"work_id":"40192478-2df8-4b65-a589-5a5546071514","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.554382Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:0f65392a0bb1f70ebb67fa4f702552f95014c3ac739727a8ca93258e1169400d","observation_id":"3dd81c17-bb17-4854-8ddf-dee505f20090","resolution":{"observed_at":"2026-08-07T22:38:38.235076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.216333Z","title":"Scaffold: Stochastic controlled averaging for federated learning","venue":null,"work_id":"98b9b0d1-36c8-4fbe-be97-cc65b8f524e1","year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.558843Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f1b8e2cfd0a7926629c120b559fdcbf36a2189dc4be168c2ac2f5a193946fd67","observation_id":"c33e3936-a5af-49bf-af36-a9a375a68976","resolution":{"observed_at":"2026-08-07T22:38:38.221087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.185701Z","title":"Osgan: One-shot distributed learning using generative adversari al networks","venue":null,"work_id":"f6435934-8b10-4004-b4bd-b648cb69a4fc","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.568504Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:01eccebed34c9f49dca960f5b5858d9fa35888dbf3184ce2998d08069a9cc1ad","observation_id":"0ce13f57-47ef-415e-9cb6-a073a99987a0","resolution":{"observed_at":"2026-08-07T22:38:38.190830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.169597Z","title":"Mediator: Memory-efficient llm merging with less parameter conflicts and uncertainty based routing","venue":null,"work_id":"5d1ba83f-62b3-4b34-84aa-28691643fe45","year":2025},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.572960Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:4930c0a43a7bccd525181d1e49d5f52112236dc6ea292959c1926017d7dcfa49","observation_id":"49a624de-9032-435d-95a7-07e2ef123c4d","resolution":{"observed_at":"2026-08-07T22:38:38.175227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.154694Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":"7fa59100-909a-45c1-bceb-7ca6c045b076","year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.577055Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:47f64504020d8b11bf14d65332c4ea5507facac3587545c780e4b030e3f499d0","observation_id":"893462f9-9959-49c0-bdf8-375d38e962b6","resolution":{"observed_at":"2026-08-07T22:38:38.158988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.140407Z","title":"Federated learning on non-iid data silos: An experimental study","venue":null,"work_id":"5e46f24d-70cb-4ebd-aa5c-05772eebd5f2","year":2022},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.581508Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:a3b4b70e5656591999d382f71e3d588d59d5f330facdf6df1ea4b8fe33e365c9","observation_id":"2eddd1ec-12d4-4424-ad85-404ed7e263e1","resolution":{"observed_at":"2026-08-07T22:38:38.145135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.125111Z","title":"Accel- erate one-shot federated learning through ensemble opti- mization","venue":null,"work_id":"a9efb321-6a62-43ec-82f7-b9472208fd18","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.585738Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:34a4a886cd436a66879ed0247c9d03af7a532aa595feaf4b7e3d94b6d0586731","observation_id":"0b4e529f-c039-4fb3-9193-c205435df0ba","resolution":{"observed_at":"2026-08-07T22:38:38.130062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1602.05629","last_updated":"2023-01-26T22:54:08Z","snapshot_observed_at":"2026-08-01T18:04:48.083997Z","submitted_at":"2016-02-17T23:40:56Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.05629","snapshot_observed_at":"2026-08-07T22:38:36.590098Z","title":"Federated learning of deep networks using model averaging","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.590098Z"},"links":{"cited_paper":"/paper/1602.05629","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:deaa5e65f5e59b1d59a8235a8977df98da343d3017ffab974fac9850317f3583","observation_id":"b4eab6b4-87ea-4c31-be5f-b544b84dbe99","resolution":{"observed_at":"2026-08-07T22:38:36.590098Z","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":"2026-08-07T22:38:38.096118Z","title":"FedTMOS: Efficient one-shot federated learning with tsetlin machine","venue":null,"work_id":"463b98c2-16b3-4c43-b54e-7a1a1f5aaa5a","year":2025},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.648374Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:9eb61406d5d87022bb6e24bfffbe63c247dc6cbdcab77b68bc856263ec50851b","observation_id":"4eea8419-a57c-44f6-afc0-f3bf5de6c68d","resolution":{"observed_at":"2026-08-07T22:38:38.100854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:36.692979Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.692979Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:bf57f0b9af3432e86a2f08e02b86dddd888862fdda578be7940863f1b9495c13","observation_id":"3cbcb0b3-c771-41ca-9fb5-b213b9aed196","resolution":{"observed_at":"2026-08-07T22:38:36.692979Z","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":"2026-08-07T22:38:38.073320Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"ad5a70f0-7c3d-482a-be79-593fe424874b","year":2022},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.745973Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:3d14efa430f7544f0e625a6df4f2070783e3ddd7370b32bde6c9a673a50d370b","observation_id":"7d28e3cb-54be-4fac-983c-3b0a595884e2","resolution":{"observed_at":"2026-08-07T22:38:38.077280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.999144Z","title":null,"venue":null,"work_id":"fcce27ad-674c-4455-b599-ac13f64cde16","year":2021},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.802629Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:18aa75de7ccbf8af2ec43cef86ab9c249a00b3269a41f1a14c605ce623e8a7c6","observation_id":"4a43aafe-3c1d-42f5-94e9-f276e5537abe","resolution":{"observed_at":"2026-08-07T22:38:38.044808Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.847424Z","title":"Bridging general and personalized federated learning through selec - tive model integration","venue":null,"work_id":"c50e6d1f-681d-4127-b346-9992f9dec5bf","year":2025},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.841169Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:ff3157b9add5633405f0ca0f6b9ba926d8892c6a5b6d737df9bccd91a9c10d17","observation_id":"afe8234c-ff28-4852-9df0-6f36220bb8a2","resolution":{"observed_at":"2026-08-07T22:38:37.918319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2006.05148","last_updated":"2020-06-09T09:43:41Z","snapshot_observed_at":"2026-08-09T03:24:41.923040Z","submitted_at":"2020-06-09T09:43:41Z","title":"XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05148","snapshot_observed_at":"2026-08-07T22:38:36.929464Z","title":"Xor mixup: Privacy- preserving data augmentation for one-shot federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.929464Z"},"links":{"cited_paper":"/paper/2006.05148","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:a1db308f51db553ff36c57d6078cbc98d18cdb0b7f64cf861738bee661f9f309","observation_id":"16d958b4-8ea5-4063-8d8e-441de436f0e5","resolution":{"observed_at":"2026-08-07T22:38:36.929464Z","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":"2026-08-07T22:38:37.722921Z","title":"Ef- fective heterogeneous federated learning via efficient hypernetwork-based weight generation","venue":null,"work_id":"ec11306d-c999-4aa7-a349-883f09039fa7","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.028166Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:daf386a6b1abf09374e87ed154ec35f473b6a2dfdd51e78b40392227d21394e1","observation_id":"9182cac8-bb50-4704-a0e7-d249edc8c218","resolution":{"observed_at":"2026-08-07T22:38:37.770752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.650339Z","title":"Federated multi-task learning","venue":null,"work_id":"e1ae816a-683b-412e-b456-68e0b16c9748","year":2017},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.032615Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:602bd3adf59023858a7389c0ef696ab74fcce895c8ca4118dbb023d388b33afd","observation_id":"837573b5-a429-47d6-abd7-ef484174adec","resolution":{"observed_at":"2026-08-07T22:38:37.655252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.635982Z","title":"Proto- typical networks for few-shot learning","venue":null,"work_id":"09ed3bc9-cf1f-4d6b-b8e1-ecbba99de452","year":2017},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.037198Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:afa6414a544d997fad9b26715359d835bb6af0a45908afe1bbc02d79c37136dd","observation_id":"c4614cb4-f883-428e-8e59-77285a9b6ed9","resolution":{"observed_at":"2026-08-07T22:38:37.640995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.621276Z","title":"Federated learning via decentralized dataset distillation in resour ce- constrained edge environments","venue":null,"work_id":"dc61f354-c9ad-4b88-a450-a05605aab13a","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.041632Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f2ea2eb4519bb4d79bc79bc6371dc756a12f7895279c6f56a5c46e8c3f79dc4e","observation_id":"80eb81c6-48f0-45b9-8e39-b612243b7edb","resolution":{"observed_at":"2026-08-07T22:38:37.625820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.607548Z","title":"One-shot federated learning without server-side training","venue":null,"work_id":"06278192-51f3-42c6-a59d-ede87eb50efa","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.046078Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:443af8dbc9b7a2cb9a17b00a660f3086f3d16c7017876d1a1042921fc1696072","observation_id":"e15563e2-9b9c-4931-aa53-d95704c8b958","resolution":{"observed_at":"2026-08-07T22:38:37.611848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.594062Z","title":"Virtual homogeneity learning: Defend- ing against data heterogeneity in federated learning","venue":null,"work_id":"223d59d1-bd3c-4837-900f-63a17041f2d4","year":2022},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.050337Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:fe9b929315c142bf3041aa91ad9c1354d5fe2fcb2104f0b9d8d5e8ee3732f860","observation_id":"02705932-ed86-431c-b226-6f3d69e94e06","resolution":{"observed_at":"2026-08-07T22:38:37.598365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.580226Z","title":"Federated learning with matched averaging","venue":null,"work_id":"dd7b1e5f-05fc-495c-90a0-9f6ea5cd2015","year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.054901Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:f9798efe952ef2084d889bd36d9aee99464d9b6545eb7d1d921581bb7946eeb4","observation_id":"2ef999bb-fbd1-4ffc-b093-260cbd8b262c","resolution":{"observed_at":"2026-08-07T22:38:37.584810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.564332Z","title":"Data-free diversity-based ensemble selection for one-shot federate d learning","venue":null,"work_id":"23b6cdd3-572d-4eee-b1ae-bf679570e77c","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.058779Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:13b481194c4b87f23235d22247d7f2add0112d510631b6aef7040a257524d6e3","observation_id":"a1591309-4c61-4613-99f3-e70b75502f07","resolution":{"observed_at":"2026-08-07T22:38:37.569584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2406.04692","last_updated":"2024-06-07T07:04:10Z","snapshot_observed_at":"2026-08-07T21:51:22.136369Z","submitted_at":"2024-06-07T07:04:10Z","title":"Mixture-of-Agents Enhances Large Language Model Capabilities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04692","snapshot_observed_at":"2026-08-07T22:38:37.062395Z","title":"Mixture-of-agents enhances large language model capabilities","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.062395Z"},"links":{"cited_paper":"/paper/2406.04692","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:7c81350f7bbf5ff3f5525fa26423070e976876ca8469b98f88beae940b2dae9d","observation_id":"f4a84aef-66b3-473c-848f-f4d75027fd9b","resolution":{"observed_at":"2026-08-07T22:38:37.062395Z","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":"2026-08-07T22:38:37.521641Z","title":"A survey on efficient federated learning methods for foundation model training","venue":null,"work_id":"759f2993-a30f-4a7b-bf00-8a8e6ef07fb7","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.068390Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:34395ca2ddf98cc6cdbfb7ee6bcd062a4d62f1102ff6634809a8489659491a70","observation_id":"7ee44b82-de7a-4b6d-b543-b30f87289916","resolution":{"observed_at":"2026-08-07T22:38:37.553180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.389669Z","title":"Open- fedllm: Training large language models on decentralized private data via federated learning","venue":null,"work_id":"8a8a6d2b-5e70-497d-921a-2ceb9deac5d9","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.073984Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:32f08061a02cfadff1bc5e063834b3475f08d8893f1334bf6f57e83a29894bed","observation_id":"451adc77-f8dd-42e3-9fa8-6e1cd839c5ef","resolution":{"observed_at":"2026-08-07T22:38:37.471567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.274601Z","title":"Bayesian nonparametric federated learning of neural networks","venue":null,"work_id":"6e0fa157-5778-4d93-b49e-d1040346fed8","year":2019},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.078236Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:3bc77b35fbeb24f0b3de182913727e3dcbac164b46158adeaf093ce1a7110a48","observation_id":"adee3365-6dfd-41ad-8f07-fb05bddfafdf","resolution":{"observed_at":"2026-08-07T22:38:37.312645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.242754Z","title":"One-shot- but-not-degraded federated learning","venue":null,"work_id":"5a36c457-247e-4d84-af0e-837c84647040","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.082705Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:7370593dda322b299d5159a1729b7c45a78e6911963d859b677776a148268e25","observation_id":"f826b49f-c1de-4e5a-84bc-c883a2e5c107","resolution":{"observed_at":"2026-08-07T22:38:37.247316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:37.224809Z","title":"Dense: Data-free one-shot federated learning","venue":null,"work_id":"95235bff-2a40-4f92-8585-b70479a0915c","year":2022},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.087070Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:d87a65bd60f0c98aea4cc2f7dd404992f9a2a8cea03f745e899f4c9274f38598","observation_id":"82f48982-1153-47f5-8138-a02f3e178879","resolution":{"observed_at":"2026-08-07T22:38:37.232078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2009.07999","last_updated":"2021-06-06T06:55:46Z","snapshot_observed_at":"2026-07-06T09:56:19.448798Z","submitted_at":"2020-09-17T01:14:47Z","title":"Distilled One-Shot Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07999","snapshot_observed_at":"2026-08-07T22:38:37.091481Z","title":"Distilled one-shot federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:37.091481Z"},"links":{"cited_paper":"/paper/2009.07999","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:6eb14df890029955b3a0f5e987484d3a7dfcb6221443995507346e1165809f4c","observation_id":"6bf1aef0-799e-431e-be9d-9de42e25985d","resolution":{"observed_at":"2026-08-07T22:38:37.091481Z","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":"2026-08-07T22:38:38.483264Z","title":"Towards a theoretical and practical understanding of one-shot federated learnin g with fisher information","venue":null,"work_id":"13f96ad6-3a10-4eaa-803a-bf33df0247cb","year":2023},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":1991,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.540904Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:8b1f206e187752d5e21036cec0ade8a3a2dc3f95ea697243e4a1092696cc3b24","observation_id":"fa2f35a6-57ff-4a9a-9bb0-2cec4b40a0fb","resolution":{"observed_at":"2026-08-07T22:38:38.515514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.704103Z","title":"Preserving privacy in federated learning with ensemble cross-domain knowledge distillation","venue":null,"work_id":"d8811129-d68d-4378-9049-a66236fac897","year":2022},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.502321Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:8787ebc9de2e610770f2d4b4836023e319b06e26669ed5a15540072849af7f6d","observation_id":"6ef7cfa4-a8e1-40c4-a9de-598bd9953a7a","resolution":{"observed_at":"2026-08-07T22:38:38.708848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.110574Z","title":"Knowledge distillation for fed - erated learning: a practical guide","venue":null,"work_id":"4e467cb9-5545-466c-987d-ab92a9158314","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.594955Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:303d4cf47e1d18d71aef055ba25f22a29cde2b4f9d1fecacd4d917fdc45659df","observation_id":"4e1d6352-883e-4c3e-8a38-c690bba571ff","resolution":{"observed_at":"2026-08-07T22:38:38.115364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2410.04810","last_updated":"2025-03-02T17:18:04Z","snapshot_observed_at":"2026-07-06T19:28:52.383175Z","submitted_at":"2024-10-07T07:45:18Z","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04810","snapshot_observed_at":"2026-08-07T22:38:36.463610Z","title":"Fedbip: Heterogeneous one-shot federated learning with personal- ized latent diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.463610Z"},"links":{"cited_paper":"/paper/2410.04810","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:6711b1a90a25431413fbac463c337c2e62504afc84a571de2489fd7291d1d8bd","observation_id":"a08681c5-aaa6-45ea-8fd9-d2ca7592dac7","resolution":{"observed_at":"2026-08-07T22:38:36.463610Z","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":"2026-08-07T22:38:38.676054Z","title":"Exploring one-shot federated learning by model inversion and token relabel with vision transformers,","venue":null,"work_id":"db0d2593-ad0b-4589-aff4-55d975a12d77","year":2025},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.516050Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:dc3d86b72dab64408364f4d66c3d87c375be9c7807d227e47a29c09f5b024bf0","observation_id":"59a7ca58-80a2-4ad2-a6e8-0ce1bfc9db3e","resolution":{"observed_at":"2026-08-07T22:38:38.680737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.201314Z","title":"Fusion learning: A one shot feder- ated learning","venue":null,"work_id":"9f311442-d91c-4e5e-8f1e-41dd92a36f43","year":2020},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.563546Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:5554a06e31e5ff31bc86b5263948e383e73345b2e4dfeaa08edb67a47a431d19","observation_id":"7f4c6e14-0caa-4520-a22f-ff9768e12e74","resolution":{"observed_at":"2026-08-07T22:38:38.205789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:39.126037Z","title":"Revisiting ensembling in one-shot federated learn- ing","venue":null,"work_id":"867649d1-bbc6-4533-ac32-e9f04b7b8728","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.390562Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:60090fe245b6c4e2ec7fe9da0fa0a4e6fbef51a14b21c1cc2e32090a47625214","observation_id":"b306a928-940c-435d-bf4d-d84350747862","resolution":{"observed_at":"2026-08-07T22:38:39.140325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.690288Z","title":"MiniLLM: Knowledge distillation of large language models","venue":null,"work_id":"dc9506f9-d50e-4596-b476-0f29593f3556","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.506569Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:bd2f6c416b1c7e99909128a8735c002aaaa63cc7a03acc8bebae8a840b399ffd","observation_id":"e6668633-d854-47e7-b14e-391f33677f10","resolution":{"observed_at":"2026-08-07T22:38:38.694783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:38:38.718715Z","title":"Differential privacy","venue":null,"work_id":"22d777cc-6453-4435-995d-5eff745dd859","year":2011},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.498375Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:6502e54c46bb414ae3a366bf0a2ca7c6785177b354fd71e3e4d3cc7c44c4e948","observation_id":"c14d3eae-54fe-4ddc-9ee5-5acd68b9b94c","resolution":{"observed_at":"2026-08-07T22:38:38.722693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2402.01862","last_updated":"2024-02-02T19:34:46Z","snapshot_observed_at":"2026-08-08T06:42:11.023982Z","submitted_at":"2024-02-02T19:34:46Z","title":"Parametric Feature Transfer: One-shot Federated Learning with Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01862","snapshot_observed_at":"2026-08-07T22:38:36.409019Z","title":"Parametric feature transfer: One-shot federated learning with foundation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.409019Z"},"links":{"cited_paper":"/paper/2402.01862","citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:8caee14e51169bec4e59a63dee436da4d6c36715bcb557c785d8ddb526e92454","observation_id":"f142190b-b819-4a97-949b-567f9e86b758","resolution":{"observed_at":"2026-08-07T22:38:36.409019Z","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":"2026-08-07T22:38:38.661344Z","title":"Calibrated one round federated learning with bayesian inference in the predic- tive space","venue":null,"work_id":"a0c83b8d-2959-47bf-94ac-a3722e38d81a","year":2024},"citing_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T22:38:36.520138Z"},"links":{"citing_paper":"/paper/2502.09104"},"observation_digest":"sha256:eb7e49963988a7dc9fc1fedfd7335a3636d501170beebbaa0a0c730ee568885e","observation_id":"a4d1ff38-19b4-4ede-ac93-1071650b8ba9","resolution":{"observed_at":"2026-08-07T22:38:38.666062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":45},"total_outbound_references":54},"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 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2502.09104."}