{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:P4GVYCPN35ZHC3DT73RQ4VA34F","short_pith_number":"pith:P4GVYCPN","canonical_record":{"source":{"id":"2204.13169","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T20:02:34Z","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"title_canon_sha256":"3d77e6f67c5d54243d9230b104e3aebc11c93ad3f3bbd299b4d2ce94f8f01c1f","abstract_canon_sha256":"851632fbfae4981482b46218b8d502469b1b5cae17a1bda96ffb8256245ee873"},"schema_version":"1.0"},"canonical_sha256":"7f0d5c09eddf72716c73fee30e541be16bda034ef856c12584b1f8f3f9198e45","source":{"kind":"arxiv","id":"2204.13169","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.13169","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"arxiv_version","alias_value":"2204.13169v3","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13169","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_12","alias_value":"P4GVYCPN35ZH","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_16","alias_value":"P4GVYCPN35ZHC3DT","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_8","alias_value":"P4GVYCPN","created_at":"2026-07-05T05:01:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:P4GVYCPN35ZHC3DT73RQ4VA34F","target":"record","payload":{"canonical_record":{"source":{"id":"2204.13169","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T20:02:34Z","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"title_canon_sha256":"3d77e6f67c5d54243d9230b104e3aebc11c93ad3f3bbd299b4d2ce94f8f01c1f","abstract_canon_sha256":"851632fbfae4981482b46218b8d502469b1b5cae17a1bda96ffb8256245ee873"},"schema_version":"1.0"},"canonical_sha256":"7f0d5c09eddf72716c73fee30e541be16bda034ef856c12584b1f8f3f9198e45","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:01.619163Z","signature_b64":"llLc/p50Nl2eII2GoDozVOCwg7RLlgIR/GJHPRvQTgxs3gOgu6QVezlv4AYT5yeHxjO1bL04Wpcgd46gtW3MBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f0d5c09eddf72716c73fee30e541be16bda034ef856c12584b1f8f3f9198e45","last_reissued_at":"2026-07-05T05:01:01.618690Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:01.618690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.13169","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":"2026-07-05T05:01:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cKWsXPTMsZ5E58JPkg75+OcgiDqaO0dtUI4+fYl+XOMvnuoMO17OPfWfR7YJdtd7b4lBS9P1I65Nrdyu5AxCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:17:13.940946Z"},"content_sha256":"cbe4388b825b8600fde96abb05899cfc32db91b30275f34b24441e1b45588ceb","schema_version":"1.0","event_id":"sha256:cbe4388b825b8600fde96abb05899cfc32db91b30275f34b24441e1b45588ceb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:P4GVYCPN35ZHC3DT73RQ4VA34F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedShuffle: Recipes for Better Use of Local Work in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Lin Xiao, Maziar Sanjabi, Michael Rabbat, Peter Richt\\'arik, Samuel Horv\\'ath","submitted_at":"2022-04-27T20:02:34Z","abstract_excerpt":"The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13169","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/2204.13169/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":"2026-07-05T05:01:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CuZWvvnTH3sdrJZrN0awzbKmN1reJnfD1WHxL0WPt+XugLiFC6mARuVuHJN4IKwOxGVEsKFZJ5SzjZ3l2/syCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:17:13.941906Z"},"content_sha256":"bed68049120b297bd57d54733b93cd4a9b1de9f7b03fa179c67b13e3475fedb6","schema_version":"1.0","event_id":"sha256:bed68049120b297bd57d54733b93cd4a9b1de9f7b03fa179c67b13e3475fedb6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/bundle.json","state_url":"https://pith.science/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/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":"2026-08-05T08:17:13Z","links":{"resolver":"https://pith.science/pith/P4GVYCPN35ZHC3DT73RQ4VA34F","bundle":"https://pith.science/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/bundle.json","state":"https://pith.science/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P4GVYCPN35ZHC3DT73RQ4VA34F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:P4GVYCPN35ZHC3DT73RQ4VA34F","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":"851632fbfae4981482b46218b8d502469b1b5cae17a1bda96ffb8256245ee873","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T20:02:34Z","title_canon_sha256":"3d77e6f67c5d54243d9230b104e3aebc11c93ad3f3bbd299b4d2ce94f8f01c1f"},"schema_version":"1.0","source":{"id":"2204.13169","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.13169","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"arxiv_version","alias_value":"2204.13169v3","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13169","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_12","alias_value":"P4GVYCPN35ZH","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_16","alias_value":"P4GVYCPN35ZHC3DT","created_at":"2026-07-05T05:01:01Z"},{"alias_kind":"pith_short_8","alias_value":"P4GVYCPN","created_at":"2026-07-05T05:01:01Z"}],"graph_snapshots":[{"event_id":"sha256:bed68049120b297bd57d54733b93cd4a9b1de9f7b03fa179c67b13e3475fedb6","target":"graph","created_at":"2026-07-05T05:01:01Z","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/2204.13169/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, ","authors_text":"Lin Xiao, Maziar Sanjabi, Michael Rabbat, Peter Richt\\'arik, Samuel Horv\\'ath","cross_cats":["cs.DC","math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T20:02:34Z","title":"FedShuffle: Recipes for Better Use of Local Work in Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13169","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:cbe4388b825b8600fde96abb05899cfc32db91b30275f34b24441e1b45588ceb","target":"record","created_at":"2026-07-05T05:01:01Z","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":"851632fbfae4981482b46218b8d502469b1b5cae17a1bda96ffb8256245ee873","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T20:02:34Z","title_canon_sha256":"3d77e6f67c5d54243d9230b104e3aebc11c93ad3f3bbd299b4d2ce94f8f01c1f"},"schema_version":"1.0","source":{"id":"2204.13169","kind":"arxiv","version":3}},"canonical_sha256":"7f0d5c09eddf72716c73fee30e541be16bda034ef856c12584b1f8f3f9198e45","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f0d5c09eddf72716c73fee30e541be16bda034ef856c12584b1f8f3f9198e45","first_computed_at":"2026-07-05T05:01:01.618690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:01:01.618690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"llLc/p50Nl2eII2GoDozVOCwg7RLlgIR/GJHPRvQTgxs3gOgu6QVezlv4AYT5yeHxjO1bL04Wpcgd46gtW3MBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:01:01.619163Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.13169","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cbe4388b825b8600fde96abb05899cfc32db91b30275f34b24441e1b45588ceb","sha256:bed68049120b297bd57d54733b93cd4a9b1de9f7b03fa179c67b13e3475fedb6"],"state_sha256":"154462b48735f3ca44ea745e0b056f021c02925a351b24c3ccdcc41b7b73b921"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"clgyMFT+pXl0sky2BdsZduEbThboLkErzmiBOBittZOQWK/sqifcL4OGd3+hkQGUr7JG9Fd4xhK/fybsOD25BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T08:17:13.950182Z","bundle_sha256":"1407859263ff167feecefd02e2e7beed13f3629d6eb2aecaf1be7080d56e77d6"}}