{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4HTLRABCOKQMWOMNWL7YHNBVOZ","short_pith_number":"pith:4HTLRABC","schema_version":"1.0","canonical_sha256":"e1e6b8802272a0cb398db2ff83b435766267f7847dcd5aff278af4cbe18c2417","source":{"kind":"arxiv","id":"2404.13238","version":2},"attestation_state":"computed","paper":{"title":"Personalized Wireless Federated Learning for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Cunhua Pan, Dusit Niyato, Feibo Jiang, Kezhi Wang, Kun Yang, Li Dong, Siwei Tu, Yubo Peng","submitted_at":"2024-04-20T02:30:21Z","abstract_excerpt":"Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges related to security and privacy. Federated Learning (FL), with its decentralized architecture, offers enhanced data privacy protection. Nevertheless, when integrated with LLMs, FL still struggles with several critical limitations, including large-scale and heterogeneous data, resource-intensive training, and substantial communication overhead. To address these challenges, this paper first presents a systematic analysis "},"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":"2404.13238","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-20T02:30:21Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f4dcad0dd85f97580aded27e3cf0d762f62111337c3a5b845de0c9797bf2fade","abstract_canon_sha256":"b0d9a9620054b63e4a98120bd3ec5626f2bb337b692e9613be5b34a6c12615b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:32.349004Z","signature_b64":"xBbSZzz7lb3+8mfJOhU1wv/tXHWsBpWCmxVdUHEFfMBvoULuvYnPyEjY2qCG2ULbSnt9obbh1lRUGVNRtPfjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1e6b8802272a0cb398db2ff83b435766267f7847dcd5aff278af4cbe18c2417","last_reissued_at":"2026-07-05T11:21:32.348541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:32.348541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalized Wireless Federated Learning for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Cunhua Pan, Dusit Niyato, Feibo Jiang, Kezhi Wang, Kun Yang, Li Dong, Siwei Tu, Yubo Peng","submitted_at":"2024-04-20T02:30:21Z","abstract_excerpt":"Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges related to security and privacy. Federated Learning (FL), with its decentralized architecture, offers enhanced data privacy protection. Nevertheless, when integrated with LLMs, FL still struggles with several critical limitations, including large-scale and heterogeneous data, resource-intensive training, and substantial communication overhead. To address these challenges, this paper first presents a systematic analysis "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.13238","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/2404.13238/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":"2404.13238","created_at":"2026-07-05T11:21:32.348599+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.13238v2","created_at":"2026-07-05T11:21:32.348599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.13238","created_at":"2026-07-05T11:21:32.348599+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HTLRABCOKQM","created_at":"2026-07-05T11:21:32.348599+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HTLRABCOKQMWOMN","created_at":"2026-07-05T11:21:32.348599+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HTLRABC","created_at":"2026-07-05T11:21:32.348599+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.09631","citing_title":"Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ","json":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ.json","graph_json":"https://pith.science/api/pith-number/4HTLRABCOKQMWOMNWL7YHNBVOZ/graph.json","events_json":"https://pith.science/api/pith-number/4HTLRABCOKQMWOMNWL7YHNBVOZ/events.json","paper":"https://pith.science/paper/4HTLRABC"},"agent_actions":{"view_html":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ","download_json":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ.json","view_paper":"https://pith.science/paper/4HTLRABC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.13238&json=true","fetch_graph":"https://pith.science/api/pith-number/4HTLRABCOKQMWOMNWL7YHNBVOZ/graph.json","fetch_events":"https://pith.science/api/pith-number/4HTLRABCOKQMWOMNWL7YHNBVOZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ/action/storage_attestation","attest_author":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ/action/author_attestation","sign_citation":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ/action/citation_signature","submit_replication":"https://pith.science/pith/4HTLRABCOKQMWOMNWL7YHNBVOZ/action/replication_record"}},"created_at":"2026-07-05T11:21:32.348599+00:00","updated_at":"2026-07-05T11:21:32.348599+00:00"}