{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WV7AQW53DGCLNO4TSFGXZJXGFJ","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":"e9eafcc72ce7793b66f5b38c99ecaf547578b0cfa14a45f174f1ccd2e95b9147","cross_cats_sorted":["cs.DC","cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-07T20:17:33Z","title_canon_sha256":"60b54cea96a5f750623e25143b883663d944677075e0e8ce14552c69f56958fa"},"schema_version":"1.0","source":{"id":"2201.02664","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.02664","created_at":"2026-07-05T04:24:57Z"},{"alias_kind":"arxiv_version","alias_value":"2201.02664v3","created_at":"2026-07-05T04:24:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.02664","created_at":"2026-07-05T04:24:57Z"},{"alias_kind":"pith_short_12","alias_value":"WV7AQW53DGCL","created_at":"2026-07-05T04:24:57Z"},{"alias_kind":"pith_short_16","alias_value":"WV7AQW53DGCLNO4T","created_at":"2026-07-05T04:24:57Z"},{"alias_kind":"pith_short_8","alias_value":"WV7AQW53","created_at":"2026-07-05T04:24:57Z"}],"graph_snapshots":[{"event_id":"sha256:051c1dcc7c59cce9a830c130a7eee602c20cffc5d89928e5053f9c2dfd629342","target":"graph","created_at":"2026-07-05T04:24:57Z","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/2201.02664/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive empirical study of the statistics of model updates in FL, as well as the role and benefits of various compression techniques. Motivated by these observations, we propose a novel method to reduce the average communication cost, which is near-optimal in many use cases, and outperforms Top-K, DRIVE, 3LC and QSGD on Stack Overflow next-word prediction, a realistic and challenging FL benchmark. This is achieved by examin","authors_text":"Jakub Kone\\v{c}n\\'y, Johannes Ball\\'e, Nicole Mitchell, Zachary Charles","cross_cats":["cs.DC","cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-07T20:17:33Z","title":"Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.02664","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":"sha256:188dc6349d1bef6c8e8fcbb02cee1ef912a6f21bba85d5911aa879eed7e34452","target":"record","created_at":"2026-07-05T04:24:57Z","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":"e9eafcc72ce7793b66f5b38c99ecaf547578b0cfa14a45f174f1ccd2e95b9147","cross_cats_sorted":["cs.DC","cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-07T20:17:33Z","title_canon_sha256":"60b54cea96a5f750623e25143b883663d944677075e0e8ce14552c69f56958fa"},"schema_version":"1.0","source":{"id":"2201.02664","kind":"arxiv","version":3}},"canonical_sha256":"b57e085bbb1984b6bb93914d7ca6e62a6eb347a72e5706aa63bd60b6fd34f196","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b57e085bbb1984b6bb93914d7ca6e62a6eb347a72e5706aa63bd60b6fd34f196","first_computed_at":"2026-07-05T04:24:57.972513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:57.972513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZeLuBZWHSplCjUddkRk3tkC/hQ2AdvBQF8XlO20/1oqgL/8+1H5GoDioeRXS9MyrdEozkeNGru5iWEPKj547CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:57.972961Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.02664","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:188dc6349d1bef6c8e8fcbb02cee1ef912a6f21bba85d5911aa879eed7e34452","sha256:051c1dcc7c59cce9a830c130a7eee602c20cffc5d89928e5053f9c2dfd629342"],"state_sha256":"cfc644fa9c430a13c677bef7f7816d214829fbe2ec5c71290ef334cedd881def"}