{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YVOHOV6PEUAILK7LEDEW2ZDJF4","short_pith_number":"pith:YVOHOV6P","schema_version":"1.0","canonical_sha256":"c55c7757cf250085abeb20c96d64692f26ccb8f4ad2eb5a795f8787f1fa559de","source":{"kind":"arxiv","id":"2411.13907","version":1},"attestation_state":"computed","paper":{"title":"Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.NE"],"primary_cat":"cs.LG","authors_text":"Dunbo Cai, Gang Hu, Yinglei Teng, Yunrui Sun","submitted_at":"2024-11-21T07:46:01Z","abstract_excerpt":"Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prolonged latency, particularly in sequential settings, where the slowest device can bottleneck the entire process due to heterogeneous resources and frequent data exchanges between clients and servers. To address these challenges, we propose the Heterogeneous Split Federated Learnin"},"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":"2411.13907","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-21T07:46:01Z","cross_cats_sorted":["cs.AI","cs.DC","cs.NE"],"title_canon_sha256":"c90f5f1e2665019dad4c671ddc404eca0d8497df38cbe374b41269cdab16a82b","abstract_canon_sha256":"114d27374e3909ad0e2641224e1b22c485aaacc8b09edc192a3bb5aa76e36911"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:38.964295Z","signature_b64":"B6PZiRdacpHm5BplDfM9ZfvvydRm3yHWckX51diQT9XTU0jv2vfo/ZRw540wzr8zG2kHlRnXW3WFDmT+a+wTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c55c7757cf250085abeb20c96d64692f26ccb8f4ad2eb5a795f8787f1fa559de","last_reissued_at":"2026-07-05T09:38:38.963813Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:38.963813Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.NE"],"primary_cat":"cs.LG","authors_text":"Dunbo Cai, Gang Hu, Yinglei Teng, Yunrui Sun","submitted_at":"2024-11-21T07:46:01Z","abstract_excerpt":"Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prolonged latency, particularly in sequential settings, where the slowest device can bottleneck the entire process due to heterogeneous resources and frequent data exchanges between clients and servers. To address these challenges, we propose the Heterogeneous Split Federated Learnin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13907","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/2411.13907/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":"2411.13907","created_at":"2026-07-05T09:38:38.963869+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.13907v1","created_at":"2026-07-05T09:38:38.963869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13907","created_at":"2026-07-05T09:38:38.963869+00:00"},{"alias_kind":"pith_short_12","alias_value":"YVOHOV6PEUAI","created_at":"2026-07-05T09:38:38.963869+00:00"},{"alias_kind":"pith_short_16","alias_value":"YVOHOV6PEUAILK7L","created_at":"2026-07-05T09:38:38.963869+00:00"},{"alias_kind":"pith_short_8","alias_value":"YVOHOV6P","created_at":"2026-07-05T09:38:38.963869+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26523","citing_title":"StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4","json":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4.json","graph_json":"https://pith.science/api/pith-number/YVOHOV6PEUAILK7LEDEW2ZDJF4/graph.json","events_json":"https://pith.science/api/pith-number/YVOHOV6PEUAILK7LEDEW2ZDJF4/events.json","paper":"https://pith.science/paper/YVOHOV6P"},"agent_actions":{"view_html":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4","download_json":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4.json","view_paper":"https://pith.science/paper/YVOHOV6P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.13907&json=true","fetch_graph":"https://pith.science/api/pith-number/YVOHOV6PEUAILK7LEDEW2ZDJF4/graph.json","fetch_events":"https://pith.science/api/pith-number/YVOHOV6PEUAILK7LEDEW2ZDJF4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4/action/storage_attestation","attest_author":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4/action/author_attestation","sign_citation":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4/action/citation_signature","submit_replication":"https://pith.science/pith/YVOHOV6PEUAILK7LEDEW2ZDJF4/action/replication_record"}},"created_at":"2026-07-05T09:38:38.963869+00:00","updated_at":"2026-07-05T09:38:38.963869+00:00"}