{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:WJPON2JHANTW6GYUU4MANZ7SBZ","short_pith_number":"pith:WJPON2JH","canonical_record":{"source":{"id":"2208.11231","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-23T23:33:38Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"31b31b73f718e21c6a83a0f0e8ece6646c99204a960caa88682eae189d251dc5","abstract_canon_sha256":"32f8ac0bba7a47b4c447a7ba71431d19f5bb48cf05a0ff1a81b03c3e1b6dd097"},"schema_version":"1.0"},"canonical_sha256":"b25ee6e92703676f1b14a71806e7f20e4840ffac6c44aea8567ae4d782847db5","source":{"kind":"arxiv","id":"2208.11231","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.11231","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"arxiv_version","alias_value":"2208.11231v2","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11231","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_12","alias_value":"WJPON2JHANTW","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_16","alias_value":"WJPON2JHANTW6GYU","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_8","alias_value":"WJPON2JH","created_at":"2026-07-05T05:22:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:WJPON2JHANTW6GYUU4MANZ7SBZ","target":"record","payload":{"canonical_record":{"source":{"id":"2208.11231","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-23T23:33:38Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"31b31b73f718e21c6a83a0f0e8ece6646c99204a960caa88682eae189d251dc5","abstract_canon_sha256":"32f8ac0bba7a47b4c447a7ba71431d19f5bb48cf05a0ff1a81b03c3e1b6dd097"},"schema_version":"1.0"},"canonical_sha256":"b25ee6e92703676f1b14a71806e7f20e4840ffac6c44aea8567ae4d782847db5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:22:10.099901Z","signature_b64":"p4nQmw2JoLIPQJRdK2g1tQK0Y1H4GgyyIMZNunYQodZdk7stMjMKWLu7PTPZdbtNo7kyjyW1WLdvXDcfdDE3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b25ee6e92703676f1b14a71806e7f20e4840ffac6c44aea8567ae4d782847db5","last_reissued_at":"2026-07-05T05:22:10.099334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:22:10.099334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.11231","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":"2026-07-05T05:22:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qLQSdRyzrXALLBmdg01QGMxokEzrQhGb/FRNPtHptTYrfdlPZ9iVWKA1Pyi6eYpkDqtopJFSz8thAqlhkERDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:23:04.499054Z"},"content_sha256":"db63206e71f23807e9b0691fabac58cbf0bb6c9457feaa3c98b645ac8ef7b5ce","schema_version":"1.0","event_id":"sha256:db63206e71f23807e9b0691fabac58cbf0bb6c9457feaa3c98b645ac8ef7b5ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:WJPON2JHANTW6GYUU4MANZ7SBZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exact Penalty Method for Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"and Geoffrey Ye Li, Shenglong Zhou","submitted_at":"2022-08-23T23:33:38Z","abstract_excerpt":"Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11231","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/2208.11231/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":"2026-07-05T05:22:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CqZ038o4oqKJ18znOnCwgY3k4xLvIwJbaxGQP3SJ1ti3r5VcpEBGWZMhDxKepXfSUjTFoDPuTxHr97OqbQnyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:23:04.499553Z"},"content_sha256":"f83704955c294bb0a18e55a1e416e0157d0a44a090be057bef53fabb7d029676","schema_version":"1.0","event_id":"sha256:f83704955c294bb0a18e55a1e416e0157d0a44a090be057bef53fabb7d029676"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/bundle.json","state_url":"https://pith.science/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/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":"2026-08-09T11:23:04Z","links":{"resolver":"https://pith.science/pith/WJPON2JHANTW6GYUU4MANZ7SBZ","bundle":"https://pith.science/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/bundle.json","state":"https://pith.science/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WJPON2JHANTW6GYUU4MANZ7SBZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WJPON2JHANTW6GYUU4MANZ7SBZ","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":"32f8ac0bba7a47b4c447a7ba71431d19f5bb48cf05a0ff1a81b03c3e1b6dd097","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-23T23:33:38Z","title_canon_sha256":"31b31b73f718e21c6a83a0f0e8ece6646c99204a960caa88682eae189d251dc5"},"schema_version":"1.0","source":{"id":"2208.11231","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.11231","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"arxiv_version","alias_value":"2208.11231v2","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11231","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_12","alias_value":"WJPON2JHANTW","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_16","alias_value":"WJPON2JHANTW6GYU","created_at":"2026-07-05T05:22:10Z"},{"alias_kind":"pith_short_8","alias_value":"WJPON2JH","created_at":"2026-07-05T05:22:10Z"}],"graph_snapshots":[{"event_id":"sha256:f83704955c294bb0a18e55a1e416e0157d0a44a090be057bef53fabb7d029676","target":"graph","created_at":"2026-07-05T05:22:10Z","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/2208.11231/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have h","authors_text":"and Geoffrey Ye Li, Shenglong Zhou","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-23T23:33:38Z","title":"Exact Penalty Method for Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11231","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:db63206e71f23807e9b0691fabac58cbf0bb6c9457feaa3c98b645ac8ef7b5ce","target":"record","created_at":"2026-07-05T05:22:10Z","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":"32f8ac0bba7a47b4c447a7ba71431d19f5bb48cf05a0ff1a81b03c3e1b6dd097","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-23T23:33:38Z","title_canon_sha256":"31b31b73f718e21c6a83a0f0e8ece6646c99204a960caa88682eae189d251dc5"},"schema_version":"1.0","source":{"id":"2208.11231","kind":"arxiv","version":2}},"canonical_sha256":"b25ee6e92703676f1b14a71806e7f20e4840ffac6c44aea8567ae4d782847db5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b25ee6e92703676f1b14a71806e7f20e4840ffac6c44aea8567ae4d782847db5","first_computed_at":"2026-07-05T05:22:10.099334Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:22:10.099334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p4nQmw2JoLIPQJRdK2g1tQK0Y1H4GgyyIMZNunYQodZdk7stMjMKWLu7PTPZdbtNo7kyjyW1WLdvXDcfdDE3BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:22:10.099901Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.11231","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db63206e71f23807e9b0691fabac58cbf0bb6c9457feaa3c98b645ac8ef7b5ce","sha256:f83704955c294bb0a18e55a1e416e0157d0a44a090be057bef53fabb7d029676"],"state_sha256":"9b73495e29b29090a30ec5d1b523a07bd6127c34290976a0c6d7a3a4300cc100"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ect9e/2x7BW6JwtIbzzSe8a4ctSWzYoGvL67GHVgUMMptKt9q4uuCPsV3ygHt/yhcIEO5+9YGj0xD80WqFfVAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T11:23:04.503836Z","bundle_sha256":"836b979429a128d3fdcdc856ce94846d610f3baa78fdae854ef9c4c93a0592d4"}}