{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5D5DC3T7VZZEKGPMESEF42BKS6","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":"fe7a1660c6247dc167acd4fb17b0e04ed7a4e9be24d5193a37185fb20b6bd849","cross_cats_sorted":["cs.LG","eess.SP","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-02-11T04:44:29Z","title_canon_sha256":"e21cfa34d55ab2bd3ef15033e39546b15ea79d1c73f707fc8a41e80876c06474"},"schema_version":"1.0","source":{"id":"2302.05599","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05599","created_at":"2026-07-05T05:40:55Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05599v1","created_at":"2026-07-05T05:40:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05599","created_at":"2026-07-05T05:40:55Z"},{"alias_kind":"pith_short_12","alias_value":"5D5DC3T7VZZE","created_at":"2026-07-05T05:40:55Z"},{"alias_kind":"pith_short_16","alias_value":"5D5DC3T7VZZEKGPM","created_at":"2026-07-05T05:40:55Z"},{"alias_kind":"pith_short_8","alias_value":"5D5DC3T7","created_at":"2026-07-05T05:40:55Z"}],"graph_snapshots":[{"event_id":"sha256:4a051980c38b0056d70d62ec6c575b7ff1e89599a4e98dc6fd9443d5a9c591d3","target":"graph","created_at":"2026-07-05T05:40:55Z","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/2302.05599/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) is a popular distributed machine learning (ML) paradigm, but is often limited by significant communication costs and edge device computation capabilities. Federated Split Learning (FSL) preserves the parallel model training principle of FL, with a reduced device computation requirement thanks to splitting the ML model between the server and clients. However, FSL still incurs very high communication overhead due to transmitting the smashed data and gradients between the clients and the server in each global round. Furthermore, the server has to maintain separate models f","authors_text":"Cong Shen, Yujia Mu","cross_cats":["cs.LG","eess.SP","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-02-11T04:44:29Z","title":"Communication and Storage Efficient Federated Split Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05599","kind":"arxiv","version":1},"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":"sha256:614899395b21cb6c4e63f82ae862a4fdc6a989bbff0f45333555e60672a0c912","target":"record","created_at":"2026-07-05T05:40:55Z","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":"fe7a1660c6247dc167acd4fb17b0e04ed7a4e9be24d5193a37185fb20b6bd849","cross_cats_sorted":["cs.LG","eess.SP","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-02-11T04:44:29Z","title_canon_sha256":"e21cfa34d55ab2bd3ef15033e39546b15ea79d1c73f707fc8a41e80876c06474"},"schema_version":"1.0","source":{"id":"2302.05599","kind":"arxiv","version":1}},"canonical_sha256":"e8fa316e7fae724519ec24885e682a97af8b2e2a391e6e46e386cf437d012783","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e8fa316e7fae724519ec24885e682a97af8b2e2a391e6e46e386cf437d012783","first_computed_at":"2026-07-05T05:40:55.744746Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:55.744746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kU1Y+iAP869Kcz0t1c0KUlHq+2hHpKIt01BfSeI0mFiyYmBpJgCfEqTqWTq4S2M75Qki433RI50scLji4FsWDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:55.745279Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.05599","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:614899395b21cb6c4e63f82ae862a4fdc6a989bbff0f45333555e60672a0c912","sha256:4a051980c38b0056d70d62ec6c575b7ff1e89599a4e98dc6fd9443d5a9c591d3"],"state_sha256":"b7f08474cc65cf1ee6aaf8182315d3f19dd0f19f8553f1c5cf173e875ca35b67"}