{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NEAOTWOH3CV4LVG6IFDI5VW7CK","short_pith_number":"pith:NEAOTWOH","canonical_record":{"source":{"id":"2212.01548","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T06:04:11Z","cross_cats_sorted":["cs.CR","cs.CV","cs.DC"],"title_canon_sha256":"f6e84555e5075b4e10c0c6bc9c66186a358f13f14da47cbe68c936986e1db947","abstract_canon_sha256":"9edee542d438fd84f136294f6c2b4d0ea7cea63b8ab23b8a2a821885c9333bc5"},"schema_version":"1.0"},"canonical_sha256":"6900e9d9c7d8abc5d4de41468ed6df12859df99c7d5aa520e8c78a89165d1069","source":{"kind":"arxiv","id":"2212.01548","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.01548","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"arxiv_version","alias_value":"2212.01548v2","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01548","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_12","alias_value":"NEAOTWOH3CV4","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_16","alias_value":"NEAOTWOH3CV4LVG6","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_8","alias_value":"NEAOTWOH","created_at":"2026-07-05T05:34:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NEAOTWOH3CV4LVG6IFDI5VW7CK","target":"record","payload":{"canonical_record":{"source":{"id":"2212.01548","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T06:04:11Z","cross_cats_sorted":["cs.CR","cs.CV","cs.DC"],"title_canon_sha256":"f6e84555e5075b4e10c0c6bc9c66186a358f13f14da47cbe68c936986e1db947","abstract_canon_sha256":"9edee542d438fd84f136294f6c2b4d0ea7cea63b8ab23b8a2a821885c9333bc5"},"schema_version":"1.0"},"canonical_sha256":"6900e9d9c7d8abc5d4de41468ed6df12859df99c7d5aa520e8c78a89165d1069","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:34:50.597367Z","signature_b64":"+BXio31/MJZ4TIoVmhk5n2bSuaTECjxxYvC1/+n9pZPoaMRSUYzAp/aazyUS25z38TzTBhEJ32PlDcx/4mklBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6900e9d9c7d8abc5d4de41468ed6df12859df99c7d5aa520e8c78a89165d1069","last_reissued_at":"2026-07-05T05:34:50.596949Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:34:50.596949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.01548","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:34:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ezcfvU1liEfXn0zReXZwzLzs4d5oOR2pi3pHSlV0r62RNFp/kcSbj4cxyNcLdwp7FKCWfkz4cSieFmzcd2AeDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:12:50.357562Z"},"content_sha256":"366a8276fd21e56f735c858cee19968a9807eba810f06b570918290129b82f45","schema_version":"1.0","event_id":"sha256:366a8276fd21e56f735c858cee19968a9807eba810f06b570918290129b82f45"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NEAOTWOH3CV4LVG6IFDI5VW7CK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CR","cs.CV","cs.DC"],"primary_cat":"cs.LG","authors_text":"Luyang Liu, Ming Yan, Mi Zhang, Samiul Alam","submitted_at":"2022-12-03T06:04:11Z","abstract_excerpt":"Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constraint not only excludes low-end clients who would otherwise make unique contributions to model training but also restrains clients from training large models due to on-device resource bottlenecks. In this work, we propose FedRolex, a partial training (PT)-based approach that enables model-heterogeneous FL and can train a global server model larger than the largest client model. At its core, FedRolex employs a rolling su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01548","kind":"arxiv","version":2},"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/2212.01548/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":"2026-07-05T05:34:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AJtEjFfr970dSSTIgGlH9QtO0U1Np+LH3I84N/RnjkY74CQX+/iZ9RW/shTJLfUP2+wXqtA2beDU4mzxvcxZCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:12:50.358315Z"},"content_sha256":"cbb35e27e80c761be4b47f34e79acb3475b8b731070c4859b047415599e2761e","schema_version":"1.0","event_id":"sha256:cbb35e27e80c761be4b47f34e79acb3475b8b731070c4859b047415599e2761e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/bundle.json","state_url":"https://pith.science/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/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":"2026-08-11T02:12:50Z","links":{"resolver":"https://pith.science/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK","bundle":"https://pith.science/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/bundle.json","state":"https://pith.science/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NEAOTWOH3CV4LVG6IFDI5VW7CK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NEAOTWOH3CV4LVG6IFDI5VW7CK","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":"9edee542d438fd84f136294f6c2b4d0ea7cea63b8ab23b8a2a821885c9333bc5","cross_cats_sorted":["cs.CR","cs.CV","cs.DC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T06:04:11Z","title_canon_sha256":"f6e84555e5075b4e10c0c6bc9c66186a358f13f14da47cbe68c936986e1db947"},"schema_version":"1.0","source":{"id":"2212.01548","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.01548","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"arxiv_version","alias_value":"2212.01548v2","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01548","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_12","alias_value":"NEAOTWOH3CV4","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_16","alias_value":"NEAOTWOH3CV4LVG6","created_at":"2026-07-05T05:34:50Z"},{"alias_kind":"pith_short_8","alias_value":"NEAOTWOH","created_at":"2026-07-05T05:34:50Z"}],"graph_snapshots":[{"event_id":"sha256:cbb35e27e80c761be4b47f34e79acb3475b8b731070c4859b047415599e2761e","target":"graph","created_at":"2026-07-05T05:34:50Z","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/2212.01548/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constraint not only excludes low-end clients who would otherwise make unique contributions to model training but also restrains clients from training large models due to on-device resource bottlenecks. In this work, we propose FedRolex, a partial training (PT)-based approach that enables model-heterogeneous FL and can train a global server model larger than the largest client model. At its core, FedRolex employs a rolling su","authors_text":"Luyang Liu, Ming Yan, Mi Zhang, Samiul Alam","cross_cats":["cs.CR","cs.CV","cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T06:04:11Z","title":"FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01548","kind":"arxiv","version":2},"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":"sha256:366a8276fd21e56f735c858cee19968a9807eba810f06b570918290129b82f45","target":"record","created_at":"2026-07-05T05:34:50Z","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":"9edee542d438fd84f136294f6c2b4d0ea7cea63b8ab23b8a2a821885c9333bc5","cross_cats_sorted":["cs.CR","cs.CV","cs.DC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T06:04:11Z","title_canon_sha256":"f6e84555e5075b4e10c0c6bc9c66186a358f13f14da47cbe68c936986e1db947"},"schema_version":"1.0","source":{"id":"2212.01548","kind":"arxiv","version":2}},"canonical_sha256":"6900e9d9c7d8abc5d4de41468ed6df12859df99c7d5aa520e8c78a89165d1069","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6900e9d9c7d8abc5d4de41468ed6df12859df99c7d5aa520e8c78a89165d1069","first_computed_at":"2026-07-05T05:34:50.596949Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:34:50.596949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+BXio31/MJZ4TIoVmhk5n2bSuaTECjxxYvC1/+n9pZPoaMRSUYzAp/aazyUS25z38TzTBhEJ32PlDcx/4mklBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:34:50.597367Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.01548","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:366a8276fd21e56f735c858cee19968a9807eba810f06b570918290129b82f45","sha256:cbb35e27e80c761be4b47f34e79acb3475b8b731070c4859b047415599e2761e"],"state_sha256":"765ad46bdadb94bb8b3499e68b151b6d8da7c07feb4ffa9de3eadac9c05c9c9d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s9SIwEP5lzxzUF7fn/5+wZ1cyX9aLMvVcH4gwrZ7aJZOURoWBdRuCU7qsU6QT+BuIhyJOThUCiLppVaEjJqXDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T02:12:50.364487Z","bundle_sha256":"26609ace2620384049bc60210e9649b2fc06a0b363088d23c16996c8fd359d29"}}