{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KQC4PCBAJU3GEVJQBN23NDYRJA","short_pith_number":"pith:KQC4PCBA","schema_version":"1.0","canonical_sha256":"5405c788204d366255300b75b68f11481f493c391c3298d5bcc0136bcbf04e1a","source":{"kind":"arxiv","id":"2305.12979","version":1},"attestation_state":"computed","paper":{"title":"When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Jingdong Xu, Lei Jiao, Lingjun Pu, Meijuan Yang, Xiaofei Wang, Xinjing Yuan","submitted_at":"2023-05-22T12:36:52Z","abstract_excerpt":"In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maxi?mizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.12979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2023-05-22T12:36:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"810aa25d575ebd70ae7851a71acd8130712e39e243c806b3257408075f665887","abstract_canon_sha256":"04936592a734a605dbae40d49e896a0e195d6d3d641cdef61ebae9b21d6d1203"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:25.251778Z","signature_b64":"1fwrXua9Ov/NWeFH6JUaWHcLGfVfKWNt/Y13p9SVGTYDMiaeYzCaJO8VksK4qKmIlVTX9cbRccjUc+gQIxmGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5405c788204d366255300b75b68f11481f493c391c3298d5bcc0136bcbf04e1a","last_reissued_at":"2026-07-05T06:12:25.251359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:25.251359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Jingdong Xu, Lei Jiao, Lingjun Pu, Meijuan Yang, Xiaofei Wang, Xinjing Yuan","submitted_at":"2023-05-22T12:36:52Z","abstract_excerpt":"In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maxi?mizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12979","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/2305.12979/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.12979","created_at":"2026-07-05T06:12:25.251432+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12979v1","created_at":"2026-07-05T06:12:25.251432+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12979","created_at":"2026-07-05T06:12:25.251432+00:00"},{"alias_kind":"pith_short_12","alias_value":"KQC4PCBAJU3G","created_at":"2026-07-05T06:12:25.251432+00:00"},{"alias_kind":"pith_short_16","alias_value":"KQC4PCBAJU3GEVJQ","created_at":"2026-07-05T06:12:25.251432+00:00"},{"alias_kind":"pith_short_8","alias_value":"KQC4PCBA","created_at":"2026-07-05T06:12:25.251432+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA","json":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA.json","graph_json":"https://pith.science/api/pith-number/KQC4PCBAJU3GEVJQBN23NDYRJA/graph.json","events_json":"https://pith.science/api/pith-number/KQC4PCBAJU3GEVJQBN23NDYRJA/events.json","paper":"https://pith.science/paper/KQC4PCBA"},"agent_actions":{"view_html":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA","download_json":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA.json","view_paper":"https://pith.science/paper/KQC4PCBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12979&json=true","fetch_graph":"https://pith.science/api/pith-number/KQC4PCBAJU3GEVJQBN23NDYRJA/graph.json","fetch_events":"https://pith.science/api/pith-number/KQC4PCBAJU3GEVJQBN23NDYRJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA/action/storage_attestation","attest_author":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA/action/author_attestation","sign_citation":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA/action/citation_signature","submit_replication":"https://pith.science/pith/KQC4PCBAJU3GEVJQBN23NDYRJA/action/replication_record"}},"created_at":"2026-07-05T06:12:25.251432+00:00","updated_at":"2026-07-05T06:12:25.251432+00:00"}