{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SC4FSYSECPAE7BWEULY2KFM3HI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c1cb48e36184c03a1aec06758c49096d43dcb9b3434c5eb3a99d7ad96a7b0643","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T14:30:59Z","title_canon_sha256":"39424c217bd1e9117a82368a2e71bacca05c0202397e8f54d22dcc9802f8b6a1"},"schema_version":"1.0","source":{"id":"2505.15579","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15579","created_at":"2026-07-05T11:06:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15579v1","created_at":"2026-07-05T11:06:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15579","created_at":"2026-07-05T11:06:48Z"},{"alias_kind":"pith_short_12","alias_value":"SC4FSYSECPAE","created_at":"2026-07-05T11:06:48Z"},{"alias_kind":"pith_short_16","alias_value":"SC4FSYSECPAE7BWE","created_at":"2026-07-05T11:06:48Z"},{"alias_kind":"pith_short_8","alias_value":"SC4FSYSE","created_at":"2026-07-05T11:06:48Z"}],"graph_snapshots":[{"event_id":"sha256:fb743e8f99838d935f9fc62155af46b9274e06955b186e6c1036ba18816f537e","target":"graph","created_at":"2026-07-05T11:06:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.15579/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approaches require a client to have labeled data for training or finetuning in order to obtain their own personalized model. In this paper we address this by proposing FLowDUP, a novel method that is able to generate a personalized model using only a forward pass with unlabeled data. The generated model parameters reside in a low-dimensional subspace, enabling efficient communication and computation. FLowDUP's learning objec","authors_text":"Christoph H. Lampert, Hossein Zakerinia, Jonathan Scott","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T14:30:59Z","title":"Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15579","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:20d23cbce7a1bf6976cabed2be3d740f4ad55ee80bdf2ea788eec56f2f78969e","target":"record","created_at":"2026-07-05T11:06:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c1cb48e36184c03a1aec06758c49096d43dcb9b3434c5eb3a99d7ad96a7b0643","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T14:30:59Z","title_canon_sha256":"39424c217bd1e9117a82368a2e71bacca05c0202397e8f54d22dcc9802f8b6a1"},"schema_version":"1.0","source":{"id":"2505.15579","kind":"arxiv","version":1}},"canonical_sha256":"90b859624413c04f86c4a2f1a5159b3a08276aabe5f9c7d073684168f4d267e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"90b859624413c04f86c4a2f1a5159b3a08276aabe5f9c7d073684168f4d267e5","first_computed_at":"2026-07-05T11:06:48.070642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:06:48.070642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rBvgpZQ3Hzun8bZtMm1ZkGoAUPO5RwytB47VxpFwr8XdDqWsPvViKFivpTzm14poHtPD6jhKXI1CfSBag0DvBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:06:48.071100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.15579","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20d23cbce7a1bf6976cabed2be3d740f4ad55ee80bdf2ea788eec56f2f78969e","sha256:fb743e8f99838d935f9fc62155af46b9274e06955b186e6c1036ba18816f537e"],"state_sha256":"87bde8ad5ccd37d60714c120db901990b820118e3938e62b1ca232b9856ce7b9"}