{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PXC3BEBI7SSREVUB2W7J46GSJW","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":"8748ace4b4a2868b710a482e2ab305825476cdbd4656854101e5f89a3beb8efb","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-08T03:58:28Z","title_canon_sha256":"574e05bccb3afa29025f58d30d7617bf4c292abc8242a4ec584175fd06005603"},"schema_version":"1.0","source":{"id":"2111.04263","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.04263","created_at":"2026-07-05T03:30:17Z"},{"alias_kind":"arxiv_version","alias_value":"2111.04263v2","created_at":"2026-07-05T03:30:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04263","created_at":"2026-07-05T03:30:17Z"},{"alias_kind":"pith_short_12","alias_value":"PXC3BEBI7SSR","created_at":"2026-07-05T03:30:17Z"},{"alias_kind":"pith_short_16","alias_value":"PXC3BEBI7SSREVUB","created_at":"2026-07-05T03:30:17Z"},{"alias_kind":"pith_short_8","alias_value":"PXC3BEBI","created_at":"2026-07-05T03:30:17Z"}],"graph_snapshots":[{"event_id":"sha256:3b36a1fc5918f7cbfaae2ec5377c908fd83a51b867724fb997053d65ad8b021b","target":"graph","created_at":"2026-07-05T03:30:17Z","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/2111.04263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for par","authors_text":"Durmus Alp Emre Acar, Matthew Mattina, Paul N. Whatmough, Ramon Matas Navarro, Venkatesh Saligrama, Yue Zhao","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-08T03:58:28Z","title":"Federated Learning Based on Dynamic Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04263","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:ea488c68aa6176500eab91fc4c9d1a3679f47df5d094cf2e0eff2c5abeb3907e","target":"record","created_at":"2026-07-05T03:30:17Z","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":"8748ace4b4a2868b710a482e2ab305825476cdbd4656854101e5f89a3beb8efb","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-08T03:58:28Z","title_canon_sha256":"574e05bccb3afa29025f58d30d7617bf4c292abc8242a4ec584175fd06005603"},"schema_version":"1.0","source":{"id":"2111.04263","kind":"arxiv","version":2}},"canonical_sha256":"7dc5b09028fca5125681d5be9e78d24da19d61e2c01e11a1e777a54e2860d2ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7dc5b09028fca5125681d5be9e78d24da19d61e2c01e11a1e777a54e2860d2ee","first_computed_at":"2026-07-05T03:30:17.883686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:30:17.883686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AFKVNe0yc3LsiBmMS1B+P88QWdNM1Xr8gkDfSim/i6E7OdQ6ihFKRWp9U8mTWPcw/B8N8TC4m42YcCH6BfxjDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:30:17.884163Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.04263","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea488c68aa6176500eab91fc4c9d1a3679f47df5d094cf2e0eff2c5abeb3907e","sha256:3b36a1fc5918f7cbfaae2ec5377c908fd83a51b867724fb997053d65ad8b021b"],"state_sha256":"5e6b83eb256e98452c6aef5645cb6351a61456f49189397769322841b9e4e609"}