{"as_of":"2026-08-16T00:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3c2f0e775080421d5105347f4a4e9f568b3e06a5b2456824e4f13aedc9a23d6c","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:21:58.335796Z","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":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.15579/citation-record","integrity":"/paper/2505.15579/integrity","json":"/paper/2505.15579/citation-record.json","paper":"/paper/2505.15579"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.12488","last_updated":"2023-01-18T08:30:06Z","snapshot_observed_at":"2026-08-12T10:37:35.790754Z","submitted_at":"2019-09-27T04:26:37Z","title":"Improving Federated Learning Personalization via Model Agnostic Meta Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12488","snapshot_observed_at":"2026-08-07T15:21:58.256225Z","title":"Kairouz, P., McMahan, H","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.256225Z"},"links":{"cited_paper":"/paper/1909.12488","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:63f054674d3b0ed80f492561444d11b1909e28793b7bb58bb72780375b872ddb","observation_id":"20bcf3ed-de00-458a-be58-dcc9321f1101","resolution":{"observed_at":"2026-08-07T15:21:58.256225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19067","last_updated":"2025-05-21T12:59:55Z","snapshot_observed_at":"2026-08-15T06:06:50.004082Z","submitted_at":"2025-01-31T11:53:16Z","title":"From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19067","snapshot_observed_at":"2026-08-07T15:21:58.289655Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.289655Z"},"links":{"cited_paper":"/paper/2501.19067","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:360d335e499bbb4922ad3047c6beb30acc2fcaac1460d53af668a2c7d0993bcf","observation_id":"4e2b1fda-f970-46d1-b360-1d182824253f","resolution":{"observed_at":"2026-08-07T15:21:58.289655Z","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":"2026-08-07T15:21:58.707448Z","title":"we have a distribution D, and the goal is to generalize from training data to the unseen data","venue":null,"work_id":"de323b80-1f38-4280-9c0c-7812e858a60e","year":2014},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.303576Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:c9669f3b300b444882fc3404c6671f23d8d1e516e13ab99c1a4bfed4446da3bd","observation_id":"f3ae8cfe-c3ad-4fc1-8ed8-a49576c459c2","resolution":{"observed_at":"2026-08-07T15:21:58.714601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T15:21:58.649763Z","title":"Parameter decomposition-approaches (Arivazhagan et al., 2019; Collins et al., 2021; Marfoq et al., 2022; Chen et al., 2023; Wu et al.,","venue":null,"work_id":"7b48bccc-38f9-4ba1-9318-d54a04fd5c37","year":2019},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.324754Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:a85d4adb3c6e1f4600c59edc99c2c0ef8158eee82c7600c5e16c1c510a3b9266","observation_id":"683a7fff-98c3-40ab-94cb-f783b4f871b8","resolution":{"observed_at":"2026-08-07T15:21:58.655870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T15:21:58.630136Z","title":"Federated multi-task approaches (Smith et al., 2017; Dinh et al., 2020; Hanzely et al., 2020; Marfoq et al., 2021; Li et al., 2021; Lin et al., 2022; Ye et al., 2023; Zhang et al.,","venue":null,"work_id":"d28824ea-cf47-4b4d-af36-f5b6ad9a3aca","year":2017},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.330562Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:3f9f9d5765dba448f0bf549631c579f36aba81e9b7077d6089c45dd052c52845","observation_id":"7d38b384-062c-468e-a2bb-4ab7b7aa9f83","resolution":{"observed_at":"2026-08-07T15:21:58.637140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T15:21:58.609943Z","title":"All of the above approaches, however, require a client to have labeled data in order to obtain a personalized model","venue":null,"work_id":"e2750a32-3b6b-4c09-a877-e1673d09be2a","year":2018},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.335796Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:0a47ddafd6b7a0cd70245021cf1f87e5f0da82b46d882616ac5200aef7dacce4","observation_id":"3187630c-3fdc-4538-b29c-5f423de28c4e","resolution":{"observed_at":"2026-08-07T15:21:58.617094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T15:21:58.669515Z","title":null,"venue":null,"work_id":"814ee006-8665-4597-854c-40069a8ecf53","year":2019},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":256,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.318864Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:7c5d211a47e047eebcb3dffdb73699d3d4aae2a0b5f15d369d4963873dad373e","observation_id":"47c1cf87-81e8-4b0d-8d7f-34f0588db62f","resolution":{"observed_at":"2026-08-07T15:21:58.674967Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T15:21:58.688505Z","title":"For all methods we tune the local learning rate ηl on validation clients","venue":null,"work_id":"168538b7-7176-4078-84ac-fde20e31557c","year":2020},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":500,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.311903Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:94b17dec5276b943c6b08b8366b43eab3e6bbf94efa12cb1356cb7cf2c3f5045","observation_id":"7cd3859c-009f-447c-8740-14304e9f3b09","resolution":{"observed_at":"2026-08-07T15:21:58.694004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"cs.lg/0411099","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:21:58.503211Z","title":"Maurer, A","venue":null,"work_id":"288697f3-8468-4af9-8e63-e3e2683ac13a","year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2004,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.270502Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:316be79e335534e93246b2637211f78454c96ff9e82ab89ff05c81572bcdafff","observation_id":"0e8628c4-e175-40ad-b57b-696d303af5cc","resolution":{"observed_at":"2026-08-07T15:21:58.514559Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-08-13T15:13:33.081929Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-07T15:21:58.276318Z","title":"Ye, R., Ni, Z., Wu, F., Chen, S., and Wang, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.276318Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:8a66fa2cffbb095dfdbbf21b73bcf43944340f4fa07d1f384af35aa631dba9f1","observation_id":"d72e80ed-6648-4454-bf9c-1243e4c0dd4d","resolution":{"observed_at":"2026-08-07T15:21:58.276318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01097","last_updated":"2019-12-09T20:02:37Z","snapshot_observed_at":"2026-08-15T08:29:55.079086Z","submitted_at":"2018-12-03T21:59:41Z","title":"LEAF: A Benchmark for Federated Settings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01097","snapshot_observed_at":"2026-08-07T15:21:58.242251Z","title":"Caruana, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.242251Z"},"links":{"cited_paper":"/paper/1812.01097","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:dd8c0ca0163c3e364ec738d45614474ae0ba47e1d61550b91d8db5dd397e1db4","observation_id":"40a6c29a-a711-4c79-ad40-0a60a0cb95bf","resolution":{"observed_at":"2026-08-07T15:21:58.242251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-08-13T23:51:40.504761Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-07T15:21:58.233966Z","title":"Bartlett, P","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.233966Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:c19ade6f60dd836630fc6ac3918bd5208b0d0089e19074164e17a516449351f4","observation_id":"bb507a23-fe5e-497a-a65f-5daebd455102","resolution":{"observed_at":"2026-08-07T15:21:58.233966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08982","last_updated":"2020-10-18T13:28:30Z","snapshot_observed_at":"2026-08-10T07:30:08.922840Z","submitted_at":"2020-10-18T13:28:30Z","title":"Federated Unsupervised Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08982","snapshot_observed_at":"2026-08-07T15:21:58.297379Z","title":"Zhang, H., Li, C., Dai, W., Zou, J., and Xiong, H","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.297379Z"},"links":{"cited_paper":"/paper/2010.08982","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:4245001ff7c9a3f6a26854c51d6aed62c4cbf36c0c8cab57046ca4b3546bac10","observation_id":"811bb885-22d9-4a8b-ae52-e2f79ee43c8d","resolution":{"observed_at":"2026-08-07T15:21:58.297379Z","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":"2026-08-07T15:21:58.728415Z","title":"UPFL: unsupervised personalized federated learning towards new clients","venue":null,"work_id":"9a610f72-d7bf-41e6-8449-8e555ab3bdc2","year":2024},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.282698Z"},"links":{"citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:90a56db530a31f97016a1b5cad30e6ef988020874f5d93c8c98a62f3a4c71398","observation_id":"4102585d-f249-44c5-b469-5a1929002de0","resolution":{"observed_at":"2026-08-07T15:21:58.734575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06430","last_updated":"2024-12-10T11:21:26Z","snapshot_observed_at":"2026-08-13T00:34:03.086172Z","submitted_at":"2024-04-09T16:23:01Z","title":"pfl-research: simulation framework for accelerating research in Private Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.06430","snapshot_observed_at":"2026-08-07T15:21:58.248378Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T15:21:58.248378Z"},"links":{"cited_paper":"/paper/2404.06430","citing_paper":"/paper/2505.15579"},"observation_digest":"sha256:c0b4338086b880f53af0cb9dcbc007b2f21451feee9616a99c856021a27d20b8","observation_id":"3dec3547-3c63-411c-8cdb-dee20fece73f","resolution":{"observed_at":"2026-08-07T15:21:58.248378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.15579","last_updated":"2025-05-21T14:30:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T10:44:24.869238Z","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":6},"total_outbound_references":15},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2505.15579."}