{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3UKZSNBGDQF2GCJ76BSUN6JIYW","short_pith_number":"pith:3UKZSNBG","canonical_record":{"source":{"id":"2212.10025","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T06:44:32Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ded0352011199c6d4d46059c8516bcf00690ea5401374dd1add621e96bee3be2","abstract_canon_sha256":"92be277d9a65f49b542ea6481b0989c6965d5c0188d7b946cc5d900db0d88e2d"},"schema_version":"1.0"},"canonical_sha256":"dd159934261c0ba3093ff06546f928c595de7ddb2559ffc22ea21cbc0d65de9f","source":{"kind":"arxiv","id":"2212.10025","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10025","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10025v2","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10025","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_12","alias_value":"3UKZSNBGDQF2","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_16","alias_value":"3UKZSNBGDQF2GCJ7","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_8","alias_value":"3UKZSNBG","created_at":"2026-07-05T06:16:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3UKZSNBGDQF2GCJ76BSUN6JIYW","target":"record","payload":{"canonical_record":{"source":{"id":"2212.10025","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T06:44:32Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ded0352011199c6d4d46059c8516bcf00690ea5401374dd1add621e96bee3be2","abstract_canon_sha256":"92be277d9a65f49b542ea6481b0989c6965d5c0188d7b946cc5d900db0d88e2d"},"schema_version":"1.0"},"canonical_sha256":"dd159934261c0ba3093ff06546f928c595de7ddb2559ffc22ea21cbc0d65de9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:16:48.625455Z","signature_b64":"FaiH2WIrTnfavEjOTNHU9jzQaWUg4zln5Q9Um/OV3ym6wLQBI3t3znZMsLU4q3xw04V9I2Ze6plwy9HxQryFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd159934261c0ba3093ff06546f928c595de7ddb2559ffc22ea21cbc0d65de9f","last_reissued_at":"2026-07-05T06:16:48.625026Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:16:48.625026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.10025","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":"2026-07-05T06:16:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rehwupUFzJORYMt8Ayan5w9s1gUQ9fzH/wSTIyhjWjlMEfZAlUviDReBruEd6f9BM7alzcsfFD3SuNM3jpwaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:18:32.496215Z"},"content_sha256":"fd6201f5c7428dc770b0c1aa79b3309f1fec0688b32ed26ba2e912121122a42b","schema_version":"1.0","event_id":"sha256:fd6201f5c7428dc770b0c1aa79b3309f1fec0688b32ed26ba2e912121122a42b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3UKZSNBGDQF2GCJ76BSUN6JIYW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Lizhen Qu, Yong Dai, Yuanhang Yang, Zenglin Xu, Zhuo Zhang","submitted_at":"2022-12-20T06:44:32Z","abstract_excerpt":"With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks. Much literature suggests fully fine-tuning pre-trained language models (PLMs) in the FL paradigm can mitigate the data heterogeneity problem and close the performance gap with centralized training. However, large PLMs bring the curse of prohibitive communication overhead and local model adaptation costs for the FL system. To this end, we introduce various parameter-efficient tuning (PETuning) methods into federated l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10025","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/2212.10025/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":"2026-07-05T06:16:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KlSY7MB8D5WHch8VYuKeD0grWgeGGIwAnLdLPvV96KCjN6qDPWA3vruonM9CZfBa2XgTXkYbfcd/zNY4d+AqCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:18:32.496709Z"},"content_sha256":"59ad3fa9613f5962b5e444828274bbfd0115e493556fe6f45b534b80f4175b43","schema_version":"1.0","event_id":"sha256:59ad3fa9613f5962b5e444828274bbfd0115e493556fe6f45b534b80f4175b43"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/bundle.json","state_url":"https://pith.science/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/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":"2026-08-09T23:18:32Z","links":{"resolver":"https://pith.science/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW","bundle":"https://pith.science/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/bundle.json","state":"https://pith.science/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3UKZSNBGDQF2GCJ76BSUN6JIYW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3UKZSNBGDQF2GCJ76BSUN6JIYW","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":"92be277d9a65f49b542ea6481b0989c6965d5c0188d7b946cc5d900db0d88e2d","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T06:44:32Z","title_canon_sha256":"ded0352011199c6d4d46059c8516bcf00690ea5401374dd1add621e96bee3be2"},"schema_version":"1.0","source":{"id":"2212.10025","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10025","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10025v2","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10025","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_12","alias_value":"3UKZSNBGDQF2","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_16","alias_value":"3UKZSNBGDQF2GCJ7","created_at":"2026-07-05T06:16:48Z"},{"alias_kind":"pith_short_8","alias_value":"3UKZSNBG","created_at":"2026-07-05T06:16:48Z"}],"graph_snapshots":[{"event_id":"sha256:59ad3fa9613f5962b5e444828274bbfd0115e493556fe6f45b534b80f4175b43","target":"graph","created_at":"2026-07-05T06:16:48Z","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/2212.10025/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks. Much literature suggests fully fine-tuning pre-trained language models (PLMs) in the FL paradigm can mitigate the data heterogeneity problem and close the performance gap with centralized training. However, large PLMs bring the curse of prohibitive communication overhead and local model adaptation costs for the FL system. To this end, we introduce various parameter-efficient tuning (PETuning) methods into federated l","authors_text":"Lizhen Qu, Yong Dai, Yuanhang Yang, Zenglin Xu, Zhuo Zhang","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T06:44:32Z","title":"When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10025","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:fd6201f5c7428dc770b0c1aa79b3309f1fec0688b32ed26ba2e912121122a42b","target":"record","created_at":"2026-07-05T06:16:48Z","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":"92be277d9a65f49b542ea6481b0989c6965d5c0188d7b946cc5d900db0d88e2d","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T06:44:32Z","title_canon_sha256":"ded0352011199c6d4d46059c8516bcf00690ea5401374dd1add621e96bee3be2"},"schema_version":"1.0","source":{"id":"2212.10025","kind":"arxiv","version":2}},"canonical_sha256":"dd159934261c0ba3093ff06546f928c595de7ddb2559ffc22ea21cbc0d65de9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd159934261c0ba3093ff06546f928c595de7ddb2559ffc22ea21cbc0d65de9f","first_computed_at":"2026-07-05T06:16:48.625026Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:16:48.625026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FaiH2WIrTnfavEjOTNHU9jzQaWUg4zln5Q9Um/OV3ym6wLQBI3t3znZMsLU4q3xw04V9I2Ze6plwy9HxQryFAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:16:48.625455Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.10025","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd6201f5c7428dc770b0c1aa79b3309f1fec0688b32ed26ba2e912121122a42b","sha256:59ad3fa9613f5962b5e444828274bbfd0115e493556fe6f45b534b80f4175b43"],"state_sha256":"356bf1f6e534dbae44046246f61f2c3b2a2fdabd1c121475e5100d6dbfaf6b01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OVoYQe5KzhKFwIMuoFJXMvJ9ahUIovX/glsBUK9fdeZLfZy4bfq/qCcANdelyGRqq7HH+S5ulnGpBe6iERqVDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T23:18:32.500869Z","bundle_sha256":"c1bfbfb8d0a31e4d6d8eb8bbcd75fbfec821f45c24a1e13a91d94d7439a2fcf6"}}