{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SFQLBNRZ2TONUCHH6B2NBNQEIM","short_pith_number":"pith:SFQLBNRZ","canonical_record":{"source":{"id":"2308.08643","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-16T19:36:01Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"11ff55e5a687acf0e58b84c576f973bfc394e91d23821f288f47b15a199c1609","abstract_canon_sha256":"86c1eee221ec22ea0228135b4a775fb38c4c8ccff0f2bb33c99e44030010688a"},"schema_version":"1.0"},"canonical_sha256":"9160b0b639d4dcda08e7f074d0b60443354dfb8e8adbde4143c2927ddcaab790","source":{"kind":"arxiv","id":"2308.08643","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.08643","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"arxiv_version","alias_value":"2308.08643v3","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.08643","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_12","alias_value":"SFQLBNRZ2TON","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_16","alias_value":"SFQLBNRZ2TONUCHH","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_8","alias_value":"SFQLBNRZ","created_at":"2026-07-05T07:05:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SFQLBNRZ2TONUCHH6B2NBNQEIM","target":"record","payload":{"canonical_record":{"source":{"id":"2308.08643","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-16T19:36:01Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"11ff55e5a687acf0e58b84c576f973bfc394e91d23821f288f47b15a199c1609","abstract_canon_sha256":"86c1eee221ec22ea0228135b4a775fb38c4c8ccff0f2bb33c99e44030010688a"},"schema_version":"1.0"},"canonical_sha256":"9160b0b639d4dcda08e7f074d0b60443354dfb8e8adbde4143c2927ddcaab790","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:46.972091Z","signature_b64":"ektTURb1m1yWiS17Q6uJx9hmy9C+Aikp7BKjL2iPcxF23Qb64Nq8Il1oS9RcHMCKK71e2H+DAV+JLHE7ZcO8Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9160b0b639d4dcda08e7f074d0b60443354dfb8e8adbde4143c2927ddcaab790","last_reissued_at":"2026-07-05T07:05:46.971635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:46.971635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.08643","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:05:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tfv7X7jefwymjBtZD/zmRgR9I782PEAjWv3Jz1ylwuzSC4v+PHDNsMM0Glv19lNcorLqcS/rOJGOpFnc4K56Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:28:21.146795Z"},"content_sha256":"85f2f6b41ce5538d866b1e23ece4b51bc3f2b5c31bcd35a871bf273c8b0b7de5","schema_version":"1.0","event_id":"sha256:85f2f6b41ce5538d866b1e23ece4b51bc3f2b5c31bcd35a871bf273c8b0b7de5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SFQLBNRZ2TONUCHH6B2NBNQEIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Dongkuan Xu, Fenglong Ma, Jiaqi Wang, Lingjuan Lyu, Liwei Che, Suhan Cui, Xingyi Yang","submitted_at":"2023-08-16T19:36:01Z","abstract_excerpt":"This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.08643","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.08643/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:05:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aed7JzL+g7CIih1ZcKW5PVKPwDDlqQxYzdRbLZx7gxtuaHji17/IlCuqefOrvZBtLJs5AjtqeIAnC2NRiEi7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:28:21.147207Z"},"content_sha256":"9923b53caebaee54af078b748fee45feef5ac4316e1bd73ffec3472b404f7504","schema_version":"1.0","event_id":"sha256:9923b53caebaee54af078b748fee45feef5ac4316e1bd73ffec3472b404f7504"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/bundle.json","state_url":"https://pith.science/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T00:28:21Z","links":{"resolver":"https://pith.science/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM","bundle":"https://pith.science/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/bundle.json","state":"https://pith.science/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SFQLBNRZ2TONUCHH6B2NBNQEIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SFQLBNRZ2TONUCHH6B2NBNQEIM","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":"86c1eee221ec22ea0228135b4a775fb38c4c8ccff0f2bb33c99e44030010688a","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-16T19:36:01Z","title_canon_sha256":"11ff55e5a687acf0e58b84c576f973bfc394e91d23821f288f47b15a199c1609"},"schema_version":"1.0","source":{"id":"2308.08643","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.08643","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"arxiv_version","alias_value":"2308.08643v3","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.08643","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_12","alias_value":"SFQLBNRZ2TON","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_16","alias_value":"SFQLBNRZ2TONUCHH","created_at":"2026-07-05T07:05:46Z"},{"alias_kind":"pith_short_8","alias_value":"SFQLBNRZ","created_at":"2026-07-05T07:05:46Z"}],"graph_snapshots":[{"event_id":"sha256:9923b53caebaee54af078b748fee45feef5ac4316e1bd73ffec3472b404f7504","target":"graph","created_at":"2026-07-05T07:05:46Z","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/2308.08643/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human i","authors_text":"Dongkuan Xu, Fenglong Ma, Jiaqi Wang, Lingjuan Lyu, Liwei Che, Suhan Cui, Xingyi Yang","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-16T19:36:01Z","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.08643","kind":"arxiv","version":3},"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":"sha256:85f2f6b41ce5538d866b1e23ece4b51bc3f2b5c31bcd35a871bf273c8b0b7de5","target":"record","created_at":"2026-07-05T07:05:46Z","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":"86c1eee221ec22ea0228135b4a775fb38c4c8ccff0f2bb33c99e44030010688a","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-16T19:36:01Z","title_canon_sha256":"11ff55e5a687acf0e58b84c576f973bfc394e91d23821f288f47b15a199c1609"},"schema_version":"1.0","source":{"id":"2308.08643","kind":"arxiv","version":3}},"canonical_sha256":"9160b0b639d4dcda08e7f074d0b60443354dfb8e8adbde4143c2927ddcaab790","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9160b0b639d4dcda08e7f074d0b60443354dfb8e8adbde4143c2927ddcaab790","first_computed_at":"2026-07-05T07:05:46.971635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:05:46.971635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ektTURb1m1yWiS17Q6uJx9hmy9C+Aikp7BKjL2iPcxF23Qb64Nq8Il1oS9RcHMCKK71e2H+DAV+JLHE7ZcO8Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:05:46.972091Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.08643","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85f2f6b41ce5538d866b1e23ece4b51bc3f2b5c31bcd35a871bf273c8b0b7de5","sha256:9923b53caebaee54af078b748fee45feef5ac4316e1bd73ffec3472b404f7504"],"state_sha256":"64fa6ad0e9d1338527ebc1c732d8641e07d13ac14a048e4a63eb2093dff4949c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jbCy2UMyeL4i9OCy5lFH/FqzNJzrEN+z6V9m8z/BkJ2zLoo3Ngaxu/BbO6qtm9vo+A/rU25pCpBT2bWEenQiDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T00:28:21.161125Z","bundle_sha256":"7b832a28f47c535c242008e42e1beb51ecca3cc071f5261ebf838799f54e6b60"}}