{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:SNIEDSNXQZ4LIQPYSQKYDLUODR","short_pith_number":"pith:SNIEDSNX","canonical_record":{"source":{"id":"2109.08119","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-16T17:13:53Z","cross_cats_sorted":[],"title_canon_sha256":"89cc17c46258a7ead4154fac1a8fb96ad81c6108359ae936c8aef320db89bf7a","abstract_canon_sha256":"21bee1fe2ec703638afb85bc886e234fd2edc0627880482ec3a2212278baecb8"},"schema_version":"1.0"},"canonical_sha256":"935041c9b78678b441f8941581ae8e1c4d16e5575e1148f61b8870d86701a8fd","source":{"kind":"arxiv","id":"2109.08119","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08119","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08119v1","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08119","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"SNIEDSNXQZ4L","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_16","alias_value":"SNIEDSNXQZ4LIQPY","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_8","alias_value":"SNIEDSNX","created_at":"2026-07-05T03:15:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:SNIEDSNXQZ4LIQPYSQKYDLUODR","target":"record","payload":{"canonical_record":{"source":{"id":"2109.08119","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-16T17:13:53Z","cross_cats_sorted":[],"title_canon_sha256":"89cc17c46258a7ead4154fac1a8fb96ad81c6108359ae936c8aef320db89bf7a","abstract_canon_sha256":"21bee1fe2ec703638afb85bc886e234fd2edc0627880482ec3a2212278baecb8"},"schema_version":"1.0"},"canonical_sha256":"935041c9b78678b441f8941581ae8e1c4d16e5575e1148f61b8870d86701a8fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:05.280549Z","signature_b64":"jwKz9tBjlIw8B7dxs0s68U5Ax2yuHb/HN8fv9qBZE4RvtowM1OmVpOPPOBJgQ5IB/TTtJS3QJfdLP8ErvC1HCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"935041c9b78678b441f8941581ae8e1c4d16e5575e1148f61b8870d86701a8fd","last_reissued_at":"2026-07-05T03:15:05.280083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:05.280083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.08119","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mJ/RArq9U7xy20I5sBhvbiX8tQbvbVL+Sb07lOF9rnwg5ck925TN8yUn18K2HFsW/oqrulKs/3KBQN1WiLp9Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:33:47.513522Z"},"content_sha256":"efb3b453ccd95de71f56d9917010f431e24b943f200965e39e9d32fdd480eba5","schema_version":"1.0","event_id":"sha256:efb3b453ccd95de71f56d9917010f431e24b943f200965e39e9d32fdd480eba5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:SNIEDSNXQZ4LIQPYSQKYDLUODR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gauri Joshi, Jianyu Wang, Tarun Chiruvolu, Yae Jee Cho","submitted_at":"2021-09-16T17:13:53Z","abstract_excerpt":"Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL, however, assumes using the same model architecture at all clients and increases the communication cost by sending/receiving models. This may not be feasible for realistic scenarios of FL. In practice, clients have highly heterogeneous system-capabilities and limited communication resources. In our work, we propose a personalized FL framework, PerFed-CKT, where clients can use heterogeneous model architectures and d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08119","kind":"arxiv","version":1},"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/2109.08119/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":"2026-07-05T03:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"okEJ+xwB8R9WV7t3gCSRv/rEddRryp8PuH3fTiqFZ7Hho6fAlKV1VyFzh1BMfzW99xRVMrH/fi0PVvD29yAIAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:33:47.514381Z"},"content_sha256":"64de1aa25dc9e64d8b9d7c8fa7ce599ec698304289deff35b20cf3ec1d749432","schema_version":"1.0","event_id":"sha256:64de1aa25dc9e64d8b9d7c8fa7ce599ec698304289deff35b20cf3ec1d749432"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/bundle.json","state_url":"https://pith.science/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/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":"2026-08-03T21:33:47Z","links":{"resolver":"https://pith.science/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR","bundle":"https://pith.science/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/bundle.json","state":"https://pith.science/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SNIEDSNXQZ4LIQPYSQKYDLUODR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:SNIEDSNXQZ4LIQPYSQKYDLUODR","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":"21bee1fe2ec703638afb85bc886e234fd2edc0627880482ec3a2212278baecb8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-16T17:13:53Z","title_canon_sha256":"89cc17c46258a7ead4154fac1a8fb96ad81c6108359ae936c8aef320db89bf7a"},"schema_version":"1.0","source":{"id":"2109.08119","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08119","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08119v1","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08119","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"SNIEDSNXQZ4L","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_16","alias_value":"SNIEDSNXQZ4LIQPY","created_at":"2026-07-05T03:15:05Z"},{"alias_kind":"pith_short_8","alias_value":"SNIEDSNX","created_at":"2026-07-05T03:15:05Z"}],"graph_snapshots":[{"event_id":"sha256:64de1aa25dc9e64d8b9d7c8fa7ce599ec698304289deff35b20cf3ec1d749432","target":"graph","created_at":"2026-07-05T03:15:05Z","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/2109.08119/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL, however, assumes using the same model architecture at all clients and increases the communication cost by sending/receiving models. This may not be feasible for realistic scenarios of FL. In practice, clients have highly heterogeneous system-capabilities and limited communication resources. In our work, we propose a personalized FL framework, PerFed-CKT, where clients can use heterogeneous model architectures and d","authors_text":"Gauri Joshi, Jianyu Wang, Tarun Chiruvolu, Yae Jee Cho","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-16T17:13:53Z","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08119","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:efb3b453ccd95de71f56d9917010f431e24b943f200965e39e9d32fdd480eba5","target":"record","created_at":"2026-07-05T03:15:05Z","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":"21bee1fe2ec703638afb85bc886e234fd2edc0627880482ec3a2212278baecb8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-16T17:13:53Z","title_canon_sha256":"89cc17c46258a7ead4154fac1a8fb96ad81c6108359ae936c8aef320db89bf7a"},"schema_version":"1.0","source":{"id":"2109.08119","kind":"arxiv","version":1}},"canonical_sha256":"935041c9b78678b441f8941581ae8e1c4d16e5575e1148f61b8870d86701a8fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"935041c9b78678b441f8941581ae8e1c4d16e5575e1148f61b8870d86701a8fd","first_computed_at":"2026-07-05T03:15:05.280083Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:15:05.280083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jwKz9tBjlIw8B7dxs0s68U5Ax2yuHb/HN8fv9qBZE4RvtowM1OmVpOPPOBJgQ5IB/TTtJS3QJfdLP8ErvC1HCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:15:05.280549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.08119","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:efb3b453ccd95de71f56d9917010f431e24b943f200965e39e9d32fdd480eba5","sha256:64de1aa25dc9e64d8b9d7c8fa7ce599ec698304289deff35b20cf3ec1d749432"],"state_sha256":"94eeaae6fa2cf87c4541f9aadb1c451d4dcd4b0533a0aaad6783a6b761e2bba0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1ekIq8NjlUT2eZ6L1f8iB2Zuj0emKPCTqOrziNa97IjZD1TntP8uvdQkXy2osbH8s7Yb7oDWQ9wejbRIUVMTDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T21:33:47.587410Z","bundle_sha256":"ceb6b07da8c2d16b64148607e499575089fcc0b1ef66becbd76cf8e816627047"}}