{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IFXRMM7RVAQIVFKBELPOYBGFSL","short_pith_number":"pith:IFXRMM7R","schema_version":"1.0","canonical_sha256":"416f1633f1a8208a954122deec04c592ea4ed413d00ad48c36597687ef842d92","source":{"kind":"arxiv","id":"2311.13381","version":1},"attestation_state":"computed","paper":{"title":"Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Jiming Chen, Qianqian Yang, Shibo He, Yuanchao Shu, Yuhao Chen, Yuxuan Yan","submitted_at":"2023-11-22T13:20:59Z","abstract_excerpt":"Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is furthe"},"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":"2311.13381","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-22T13:20:59Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"e30f2b4b1b8a30f782dbafde30d6f6e088ea18770d567d7ab85e9cdae59a1f5b","abstract_canon_sha256":"3fd5ec2767f7bd158d69a06893bd59959971663a5297efe048842cdb95662773"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:28.722859Z","signature_b64":"rBA4gk5XqBZ/jwHM1OUAqBO3SB02LlIMAicT62bs9GV6XOpc7dGRApK3i+qSfjAOtLj5FVBL1uzdmQUSSM5lAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"416f1633f1a8208a954122deec04c592ea4ed413d00ad48c36597687ef842d92","last_reissued_at":"2026-07-05T07:15:28.722389Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:28.722389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Jiming Chen, Qianqian Yang, Shibo He, Yuanchao Shu, Yuhao Chen, Yuxuan Yan","submitted_at":"2023-11-22T13:20:59Z","abstract_excerpt":"Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is furthe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.13381","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/2311.13381/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":"2311.13381","created_at":"2026-07-05T07:15:28.722448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.13381v1","created_at":"2026-07-05T07:15:28.722448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.13381","created_at":"2026-07-05T07:15:28.722448+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFXRMM7RVAQI","created_at":"2026-07-05T07:15:28.722448+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFXRMM7RVAQIVFKB","created_at":"2026-07-05T07:15:28.722448+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFXRMM7R","created_at":"2026-07-05T07:15:28.722448+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/IFXRMM7RVAQIVFKBELPOYBGFSL","json":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL.json","graph_json":"https://pith.science/api/pith-number/IFXRMM7RVAQIVFKBELPOYBGFSL/graph.json","events_json":"https://pith.science/api/pith-number/IFXRMM7RVAQIVFKBELPOYBGFSL/events.json","paper":"https://pith.science/paper/IFXRMM7R"},"agent_actions":{"view_html":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL","download_json":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL.json","view_paper":"https://pith.science/paper/IFXRMM7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.13381&json=true","fetch_graph":"https://pith.science/api/pith-number/IFXRMM7RVAQIVFKBELPOYBGFSL/graph.json","fetch_events":"https://pith.science/api/pith-number/IFXRMM7RVAQIVFKBELPOYBGFSL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL/action/storage_attestation","attest_author":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL/action/author_attestation","sign_citation":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL/action/citation_signature","submit_replication":"https://pith.science/pith/IFXRMM7RVAQIVFKBELPOYBGFSL/action/replication_record"}},"created_at":"2026-07-05T07:15:28.722448+00:00","updated_at":"2026-07-05T07:15:28.722448+00:00"}