{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:A4U4NY44L237H4TGLBULDK4YE4","short_pith_number":"pith:A4U4NY44","canonical_record":{"source":{"id":"2503.00813","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-02T09:39:02Z","cross_cats_sorted":[],"title_canon_sha256":"3b0372da7a1d04e94937baef01ebb63f9b0a864104436014b2301e31d2edc15f","abstract_canon_sha256":"58f394e8edf252870bf8fe0708a2b67a8baf17c0b1a6bcabc4f41e686dae28e6"},"schema_version":"1.0"},"canonical_sha256":"0729c6e39c5eb7f3f2665868b1ab982721c6b97b35e64037c5e9707c00bd5c1d","source":{"kind":"arxiv","id":"2503.00813","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.00813","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"arxiv_version","alias_value":"2503.00813v1","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.00813","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_12","alias_value":"A4U4NY44L237","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_16","alias_value":"A4U4NY44L237H4TG","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_8","alias_value":"A4U4NY44","created_at":"2026-07-05T10:22:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:A4U4NY44L237H4TGLBULDK4YE4","target":"record","payload":{"canonical_record":{"source":{"id":"2503.00813","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-02T09:39:02Z","cross_cats_sorted":[],"title_canon_sha256":"3b0372da7a1d04e94937baef01ebb63f9b0a864104436014b2301e31d2edc15f","abstract_canon_sha256":"58f394e8edf252870bf8fe0708a2b67a8baf17c0b1a6bcabc4f41e686dae28e6"},"schema_version":"1.0"},"canonical_sha256":"0729c6e39c5eb7f3f2665868b1ab982721c6b97b35e64037c5e9707c00bd5c1d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:30.166502Z","signature_b64":"6dmh3usGFEQySKjLCoCj69BtDENBXcsTh80OOVKhQWhe1wJ6rxJT8vLn2De4nVYu3vlTzYkvR39nsWviC3nSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0729c6e39c5eb7f3f2665868b1ab982721c6b97b35e64037c5e9707c00bd5c1d","last_reissued_at":"2026-07-05T10:22:30.166017Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:30.166017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.00813","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":"2026-07-05T10:22:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXt3zo9ApgwDelFfhQy7yendFLCbJwqda1fXo3P9ZbTl2/BrxjQJ4GhzghU/2skW+iHZsKMU0MbGYlcf40lgDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:30:23.531458Z"},"content_sha256":"5ebb358f33104acbf8f7417e52d3afff0f09a6c345df9e22535766c948dc6c74","schema_version":"1.0","event_id":"sha256:5ebb358f33104acbf8f7417e52d3afff0f09a6c345df9e22535766c948dc6c74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:A4U4NY44L237H4TGLBULDK4YE4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Benben Liu, Qianli Liu, Xin Yao, Zhaorui Zhang","submitted_at":"2025-03-02T09:39:02Z","abstract_excerpt":"Federated learning systems have been identified as an efficient approach to scaling distributed model training with a large amount of participants or data owners while guaranteeing data privacy. To apply the current most popular pre-trained large language models to other domains with data privacy guarantee requirements, existing works propose fine-tuning the pre-trained large language models in federated learning environments across data owners using the parameter efficient fine-tuning approaches, LoRA. To address the resource and data heterogeneous issues for the participants, previous works "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.00813","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/2503.00813/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":"2026-07-05T10:22:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lfd5GzLLzyUSha/k5wZ/pyt7oaRbS26acU2BeBefbV4aBj5FXFgZmoqWXQQ9CrSUpoXpIbs0KhItVqFhT0IeAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:30:23.532029Z"},"content_sha256":"4f30ef60ed7774f9d67a9bb0166e5a3a88b963144bb331d5ddc34adb963b35db","schema_version":"1.0","event_id":"sha256:4f30ef60ed7774f9d67a9bb0166e5a3a88b963144bb331d5ddc34adb963b35db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A4U4NY44L237H4TGLBULDK4YE4/bundle.json","state_url":"https://pith.science/pith/A4U4NY44L237H4TGLBULDK4YE4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A4U4NY44L237H4TGLBULDK4YE4/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":"2026-08-14T07:30:23Z","links":{"resolver":"https://pith.science/pith/A4U4NY44L237H4TGLBULDK4YE4","bundle":"https://pith.science/pith/A4U4NY44L237H4TGLBULDK4YE4/bundle.json","state":"https://pith.science/pith/A4U4NY44L237H4TGLBULDK4YE4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A4U4NY44L237H4TGLBULDK4YE4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:A4U4NY44L237H4TGLBULDK4YE4","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":"58f394e8edf252870bf8fe0708a2b67a8baf17c0b1a6bcabc4f41e686dae28e6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-02T09:39:02Z","title_canon_sha256":"3b0372da7a1d04e94937baef01ebb63f9b0a864104436014b2301e31d2edc15f"},"schema_version":"1.0","source":{"id":"2503.00813","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.00813","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"arxiv_version","alias_value":"2503.00813v1","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.00813","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_12","alias_value":"A4U4NY44L237","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_16","alias_value":"A4U4NY44L237H4TG","created_at":"2026-07-05T10:22:30Z"},{"alias_kind":"pith_short_8","alias_value":"A4U4NY44","created_at":"2026-07-05T10:22:30Z"}],"graph_snapshots":[{"event_id":"sha256:4f30ef60ed7774f9d67a9bb0166e5a3a88b963144bb331d5ddc34adb963b35db","target":"graph","created_at":"2026-07-05T10:22:30Z","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/2503.00813/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning systems have been identified as an efficient approach to scaling distributed model training with a large amount of participants or data owners while guaranteeing data privacy. To apply the current most popular pre-trained large language models to other domains with data privacy guarantee requirements, existing works propose fine-tuning the pre-trained large language models in federated learning environments across data owners using the parameter efficient fine-tuning approaches, LoRA. To address the resource and data heterogeneous issues for the participants, previous works ","authors_text":"Benben Liu, Qianli Liu, Xin Yao, Zhaorui Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-02T09:39:02Z","title":"HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.00813","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":"sha256:5ebb358f33104acbf8f7417e52d3afff0f09a6c345df9e22535766c948dc6c74","target":"record","created_at":"2026-07-05T10:22:30Z","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":"58f394e8edf252870bf8fe0708a2b67a8baf17c0b1a6bcabc4f41e686dae28e6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-02T09:39:02Z","title_canon_sha256":"3b0372da7a1d04e94937baef01ebb63f9b0a864104436014b2301e31d2edc15f"},"schema_version":"1.0","source":{"id":"2503.00813","kind":"arxiv","version":1}},"canonical_sha256":"0729c6e39c5eb7f3f2665868b1ab982721c6b97b35e64037c5e9707c00bd5c1d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0729c6e39c5eb7f3f2665868b1ab982721c6b97b35e64037c5e9707c00bd5c1d","first_computed_at":"2026-07-05T10:22:30.166017Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:30.166017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6dmh3usGFEQySKjLCoCj69BtDENBXcsTh80OOVKhQWhe1wJ6rxJT8vLn2De4nVYu3vlTzYkvR39nsWviC3nSBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:30.166502Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.00813","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ebb358f33104acbf8f7417e52d3afff0f09a6c345df9e22535766c948dc6c74","sha256:4f30ef60ed7774f9d67a9bb0166e5a3a88b963144bb331d5ddc34adb963b35db"],"state_sha256":"b81b21f1b080d0b5f4b246cd6e89b6ad75c20f64565c01bd53031d6c4cac0a90"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zrmz/NRV/GJGr4Nk3pHimMiJkS6KDxKYziFqoduQs8icySumce6tjIstHo1wu/ppggiaIP6iu5xYfuMurwjkCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T07:30:23.537300Z","bundle_sha256":"93bdbb46421bcf717b02c8ad0d9586f397852cff4235396ee0489b6fb70c0756"}}