{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5D5DC3T7VZZEKGPMESEF42BKS6","short_pith_number":"pith:5D5DC3T7","canonical_record":{"source":{"id":"2302.05599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-02-11T04:44:29Z","cross_cats_sorted":["cs.LG","eess.SP","math.IT","stat.ML"],"title_canon_sha256":"e21cfa34d55ab2bd3ef15033e39546b15ea79d1c73f707fc8a41e80876c06474","abstract_canon_sha256":"fe7a1660c6247dc167acd4fb17b0e04ed7a4e9be24d5193a37185fb20b6bd849"},"schema_version":"1.0"},"canonical_sha256":"e8fa316e7fae724519ec24885e682a97af8b2e2a391e6e46e386cf437d012783","source":{"kind":"arxiv","id":"2302.05599","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"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5D5DC3T7VZZEKGPMESEF42BKS6","target":"record","payload":{"canonical_record":{"source":{"id":"2302.05599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-02-11T04:44:29Z","cross_cats_sorted":["cs.LG","eess.SP","math.IT","stat.ML"],"title_canon_sha256":"e21cfa34d55ab2bd3ef15033e39546b15ea79d1c73f707fc8a41e80876c06474","abstract_canon_sha256":"fe7a1660c6247dc167acd4fb17b0e04ed7a4e9be24d5193a37185fb20b6bd849"},"schema_version":"1.0"},"canonical_sha256":"e8fa316e7fae724519ec24885e682a97af8b2e2a391e6e46e386cf437d012783","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:55.745279Z","signature_b64":"kU1Y+iAP869Kcz0t1c0KUlHq+2hHpKIt01BfSeI0mFiyYmBpJgCfEqTqWTq4S2M75Qki433RI50scLji4FsWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8fa316e7fae724519ec24885e682a97af8b2e2a391e6e46e386cf437d012783","last_reissued_at":"2026-07-05T05:40:55.744746Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:55.744746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.05599","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:40:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V2fu8iwv3IDeeVPzhsWszGq9WhL6yGARNfZVmYY5vosviYE1ouKrFmURMaY3Nwsfbg2sjq6143gzml26wJsXDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T11:29:19.397355Z"},"content_sha256":"614899395b21cb6c4e63f82ae862a4fdc6a989bbff0f45333555e60672a0c912","schema_version":"1.0","event_id":"sha256:614899395b21cb6c4e63f82ae862a4fdc6a989bbff0f45333555e60672a0c912"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5D5DC3T7VZZEKGPMESEF42BKS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Communication and Storage Efficient Federated Split Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Cong Shen, Yujia Mu","submitted_at":"2023-02-11T04:44:29Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05599","kind":"arxiv","version":1},"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/2302.05599/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":"2026-07-05T05:40:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VrnZkgk2I8jAHrzsjXcMAu0DKvcCCYEdrh9I+SDL94ElowDsoOP9FhzaIb2ksCOKHgK0rzbixnDIUQVA5Sb0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T11:29:19.397865Z"},"content_sha256":"4a051980c38b0056d70d62ec6c575b7ff1e89599a4e98dc6fd9443d5a9c591d3","schema_version":"1.0","event_id":"sha256:4a051980c38b0056d70d62ec6c575b7ff1e89599a4e98dc6fd9443d5a9c591d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5D5DC3T7VZZEKGPMESEF42BKS6/bundle.json","state_url":"https://pith.science/pith/5D5DC3T7VZZEKGPMESEF42BKS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5D5DC3T7VZZEKGPMESEF42BKS6/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":"2026-08-23T11:29:19Z","links":{"resolver":"https://pith.science/pith/5D5DC3T7VZZEKGPMESEF42BKS6","bundle":"https://pith.science/pith/5D5DC3T7VZZEKGPMESEF42BKS6/bundle.json","state":"https://pith.science/pith/5D5DC3T7VZZEKGPMESEF42BKS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5D5DC3T7VZZEKGPMESEF42BKS6/bundle.json"},"state":{"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"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHNQ8PmMClEpJKvotbYY+J9JHr7ld2qkDf3KEUMokEdvc/3FD9V3GqDxoxsbkS0W5TSZsgn0XuWZK2WPN6rKBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T11:29:19.402944Z","bundle_sha256":"74758e8a3de4b25a113c2dbf3c9ca38c50940e22198553f238c9591ea4a20920"}}