{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PH6ZSKSZK2TFNB3M5GTNK5DVEW","short_pith_number":"pith:PH6ZSKSZ","schema_version":"1.0","canonical_sha256":"79fd992a5956a656876ce9a6d574752588452442dca8d77fac63d8b3cbbc3cc2","source":{"kind":"arxiv","id":"2606.03209","version":1},"attestation_state":"computed","paper":{"title":"DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feng Li, Jun Luo, Kai Han, Kai Wang, Shaowei Li, Yunsheng Yuan, Zheng Zhang, Zhongyuan Sun","submitted_at":"2026-06-02T06:08:31Z","abstract_excerpt":"Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often distributed across multiple clients, decentralized fine-tuning offers a natural paradigm for collaborative adaptation without a central server. However, enabling full-parameter fine-tuning (FPFT) in this decentralized setting is difficult: FPFT provides strong adaptation capacity but incurs prohibitive resource consumption for billion-scale models. Existing decentralized LLM fine-tuning methods therefore mainly rely on parameter-efficient update"},"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":"2606.03209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T06:08:31Z","cross_cats_sorted":[],"title_canon_sha256":"bd5f5e03631a3e3ec35a56687203539fb79f279ec7bb4f5257b1373ffae44ad9","abstract_canon_sha256":"b62973956a2ae268576c9043f92c2c6b82309f4d731cfae3c5675209496e8592"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:34.982366Z","signature_b64":"NNJI3IeBhOcNlPnmODqNaG3Q13paZvRixKIfeq9e82a8RV0ZddmV5PMd3Iwy+5QeBqo4qT7a6De6dv0CmO9nDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79fd992a5956a656876ce9a6d574752588452442dca8d77fac63d8b3cbbc3cc2","last_reissued_at":"2026-06-03T01:05:34.981955Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:34.981955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feng Li, Jun Luo, Kai Han, Kai Wang, Shaowei Li, Yunsheng Yuan, Zheng Zhang, Zhongyuan Sun","submitted_at":"2026-06-02T06:08:31Z","abstract_excerpt":"Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often distributed across multiple clients, decentralized fine-tuning offers a natural paradigm for collaborative adaptation without a central server. However, enabling full-parameter fine-tuning (FPFT) in this decentralized setting is difficult: FPFT provides strong adaptation capacity but incurs prohibitive resource consumption for billion-scale models. Existing decentralized LLM fine-tuning methods therefore mainly rely on parameter-efficient update"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03209","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/2606.03209/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":"2606.03209","created_at":"2026-06-03T01:05:34.982003+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03209v1","created_at":"2026-06-03T01:05:34.982003+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03209","created_at":"2026-06-03T01:05:34.982003+00:00"},{"alias_kind":"pith_short_12","alias_value":"PH6ZSKSZK2TF","created_at":"2026-06-03T01:05:34.982003+00:00"},{"alias_kind":"pith_short_16","alias_value":"PH6ZSKSZK2TFNB3M","created_at":"2026-06-03T01:05:34.982003+00:00"},{"alias_kind":"pith_short_8","alias_value":"PH6ZSKSZ","created_at":"2026-06-03T01:05:34.982003+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/PH6ZSKSZK2TFNB3M5GTNK5DVEW","json":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW.json","graph_json":"https://pith.science/api/pith-number/PH6ZSKSZK2TFNB3M5GTNK5DVEW/graph.json","events_json":"https://pith.science/api/pith-number/PH6ZSKSZK2TFNB3M5GTNK5DVEW/events.json","paper":"https://pith.science/paper/PH6ZSKSZ"},"agent_actions":{"view_html":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW","download_json":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW.json","view_paper":"https://pith.science/paper/PH6ZSKSZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03209&json=true","fetch_graph":"https://pith.science/api/pith-number/PH6ZSKSZK2TFNB3M5GTNK5DVEW/graph.json","fetch_events":"https://pith.science/api/pith-number/PH6ZSKSZK2TFNB3M5GTNK5DVEW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW/action/storage_attestation","attest_author":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW/action/author_attestation","sign_citation":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW/action/citation_signature","submit_replication":"https://pith.science/pith/PH6ZSKSZK2TFNB3M5GTNK5DVEW/action/replication_record"}},"created_at":"2026-06-03T01:05:34.982003+00:00","updated_at":"2026-06-03T01:05:34.982003+00:00"}