{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PWSGBMLI7PJ4TA5SOZD4ARLTVX","short_pith_number":"pith:PWSGBMLI","canonical_record":{"source":{"id":"2505.24773","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T16:35:32Z","cross_cats_sorted":[],"title_canon_sha256":"666eb283951b44a2616bdcb7c2d2895df5786c3eb63feb2933872a9047aa4577","abstract_canon_sha256":"568a4102fd87fbdd7e468bbb4d8f3d22262879076f890589a8ca1106f5c0f339"},"schema_version":"1.0"},"canonical_sha256":"7da460b168fbd3c983b27647c04573adef01f2d6057feb55a920d20e640feb9e","source":{"kind":"arxiv","id":"2505.24773","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24773","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24773v2","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24773","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_12","alias_value":"PWSGBMLI7PJ4","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_16","alias_value":"PWSGBMLI7PJ4TA5S","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_8","alias_value":"PWSGBMLI","created_at":"2026-07-05T11:56:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PWSGBMLI7PJ4TA5SOZD4ARLTVX","target":"record","payload":{"canonical_record":{"source":{"id":"2505.24773","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T16:35:32Z","cross_cats_sorted":[],"title_canon_sha256":"666eb283951b44a2616bdcb7c2d2895df5786c3eb63feb2933872a9047aa4577","abstract_canon_sha256":"568a4102fd87fbdd7e468bbb4d8f3d22262879076f890589a8ca1106f5c0f339"},"schema_version":"1.0"},"canonical_sha256":"7da460b168fbd3c983b27647c04573adef01f2d6057feb55a920d20e640feb9e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:19.910070Z","signature_b64":"hlkjX3+b/J3pJJ9S+wPJdaFNqL1HOanNB8v9y6Qzy3hCjTsMs7qlSecfweTeYAYbk7piS+0R6rvtz4gSm6kEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7da460b168fbd3c983b27647c04573adef01f2d6057feb55a920d20e640feb9e","last_reissued_at":"2026-07-05T11:56:19.909661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:19.909661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.24773","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:56:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TLBDx60WORoZrIaU7ScoV7MC6KqlB1jkg2GskQW1JnYH0tYapn+yQEtf9Mg9Bc/MR1oZOMrQRaoDl1uhFrSCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:44:00.186429Z"},"content_sha256":"15f2cbee01fc527d65e316317d6e54b6d8a6bf35359e68eac7fcb65140d2805c","schema_version":"1.0","event_id":"sha256:15f2cbee01fc527d65e316317d6e54b6d8a6bf35359e68eac7fcb65140d2805c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PWSGBMLI7PJ4TA5SOZD4ARLTVX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Xiaoyi Pang, Yajie Zhou, Zhibo Wang","submitted_at":"2025-05-30T16:35:32Z","abstract_excerpt":"Federated fine-tuning has emerged as a promising approach to adapt foundation models to downstream tasks using decentralized data. However, real-world deployment remains challenging due to the high computational and communication demands of fine-tuning Large Language Models (LLMs) on clients with data and system resources that are heterogeneous and constrained. In such settings, the global model's performance is often bottlenecked by the weakest clients and further degraded by the non-IID nature of local data. Although existing methods leverage parameter-efficient techniques such as Low-Rank A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24773","kind":"arxiv","version":2},"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/2505.24773/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":"2026-07-05T11:56:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T4SbdcUNBcOfyVNNIm7DQFZ/g8lV1moQxxQx0AcFMmXoLMLen7RH0X+A/GSY3PK1/NfLDP5gJ9L5vRpt9ZGaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:44:00.187172Z"},"content_sha256":"0d9e5f2b808a5a75ecee14c5d842337fae8ace88e2f6f4a6849e4ee0c8c15aa6","schema_version":"1.0","event_id":"sha256:0d9e5f2b808a5a75ecee14c5d842337fae8ace88e2f6f4a6849e4ee0c8c15aa6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/bundle.json","state_url":"https://pith.science/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/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":"2026-08-07T22:44:00Z","links":{"resolver":"https://pith.science/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX","bundle":"https://pith.science/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/bundle.json","state":"https://pith.science/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PWSGBMLI7PJ4TA5SOZD4ARLTVX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PWSGBMLI7PJ4TA5SOZD4ARLTVX","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":"568a4102fd87fbdd7e468bbb4d8f3d22262879076f890589a8ca1106f5c0f339","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T16:35:32Z","title_canon_sha256":"666eb283951b44a2616bdcb7c2d2895df5786c3eb63feb2933872a9047aa4577"},"schema_version":"1.0","source":{"id":"2505.24773","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24773","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24773v2","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24773","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_12","alias_value":"PWSGBMLI7PJ4","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_16","alias_value":"PWSGBMLI7PJ4TA5S","created_at":"2026-07-05T11:56:19Z"},{"alias_kind":"pith_short_8","alias_value":"PWSGBMLI","created_at":"2026-07-05T11:56:19Z"}],"graph_snapshots":[{"event_id":"sha256:0d9e5f2b808a5a75ecee14c5d842337fae8ace88e2f6f4a6849e4ee0c8c15aa6","target":"graph","created_at":"2026-07-05T11:56:19Z","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/2505.24773/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated fine-tuning has emerged as a promising approach to adapt foundation models to downstream tasks using decentralized data. However, real-world deployment remains challenging due to the high computational and communication demands of fine-tuning Large Language Models (LLMs) on clients with data and system resources that are heterogeneous and constrained. In such settings, the global model's performance is often bottlenecked by the weakest clients and further degraded by the non-IID nature of local data. Although existing methods leverage parameter-efficient techniques such as Low-Rank A","authors_text":"Xiaoyi Pang, Yajie Zhou, Zhibo Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T16:35:32Z","title":"AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24773","kind":"arxiv","version":2},"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":"sha256:15f2cbee01fc527d65e316317d6e54b6d8a6bf35359e68eac7fcb65140d2805c","target":"record","created_at":"2026-07-05T11:56:19Z","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":"568a4102fd87fbdd7e468bbb4d8f3d22262879076f890589a8ca1106f5c0f339","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T16:35:32Z","title_canon_sha256":"666eb283951b44a2616bdcb7c2d2895df5786c3eb63feb2933872a9047aa4577"},"schema_version":"1.0","source":{"id":"2505.24773","kind":"arxiv","version":2}},"canonical_sha256":"7da460b168fbd3c983b27647c04573adef01f2d6057feb55a920d20e640feb9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7da460b168fbd3c983b27647c04573adef01f2d6057feb55a920d20e640feb9e","first_computed_at":"2026-07-05T11:56:19.909661Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:56:19.909661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hlkjX3+b/J3pJJ9S+wPJdaFNqL1HOanNB8v9y6Qzy3hCjTsMs7qlSecfweTeYAYbk7piS+0R6rvtz4gSm6kEAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:56:19.910070Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24773","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15f2cbee01fc527d65e316317d6e54b6d8a6bf35359e68eac7fcb65140d2805c","sha256:0d9e5f2b808a5a75ecee14c5d842337fae8ace88e2f6f4a6849e4ee0c8c15aa6"],"state_sha256":"70220d488f4d333d537907b06a42e54b034997ad6ee17e20f427eb8a7c7a06ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E5JU2SBxsA+PPUN0mCU3URy+EVAwVh2lnX9XTC4X/EA0dx1WsPD13upmPZjAV/eQVK1kTID2Lpi/reBnfsMHBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:44:00.193160Z","bundle_sha256":"fe866378f195a51a5605043b27557a1fe40504103b8041a00f7fbec98c18ac56"}}