{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LVVYWMGIRVWQU3USAZKXTLBFA3","short_pith_number":"pith:LVVYWMGI","canonical_record":{"source":{"id":"2405.15551","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T13:37:48Z","cross_cats_sorted":[],"title_canon_sha256":"162aaab58674268372bbbcbe3bae2ae98de89446b46f426a2aafd02cc0c312da","abstract_canon_sha256":"6ae78c0e62891412c33a56636aa8d10e5612d519ec6fbd03851dd0b10cb542be"},"schema_version":"1.0"},"canonical_sha256":"5d6b8b30c88d6d0a6e92065579ac2506f379fa06d87d42440c5e7f1993f6c987","source":{"kind":"arxiv","id":"2405.15551","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.15551","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2405.15551v2","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15551","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"LVVYWMGIRVWQ","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"LVVYWMGIRVWQU3US","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"LVVYWMGI","created_at":"2026-07-05T09:23:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LVVYWMGIRVWQU3USAZKXTLBFA3","target":"record","payload":{"canonical_record":{"source":{"id":"2405.15551","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T13:37:48Z","cross_cats_sorted":[],"title_canon_sha256":"162aaab58674268372bbbcbe3bae2ae98de89446b46f426a2aafd02cc0c312da","abstract_canon_sha256":"6ae78c0e62891412c33a56636aa8d10e5612d519ec6fbd03851dd0b10cb542be"},"schema_version":"1.0"},"canonical_sha256":"5d6b8b30c88d6d0a6e92065579ac2506f379fa06d87d42440c5e7f1993f6c987","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:50.793259Z","signature_b64":"Yx55K5WWbVooP2+nLHAJafGyK/dySPyaW8yoSdwk/eIFJJWbYWVgD7XN9QRyfskI4cEbCW4ytW5Z9TkR/t4HAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d6b8b30c88d6d0a6e92065579ac2506f379fa06d87d42440c5e7f1993f6c987","last_reissued_at":"2026-07-05T09:23:50.792722Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:50.792722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.15551","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":"2026-07-05T09:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WzUNE9Vid0pG1zb07xY0LVLgPtdIatlFwOqTu4WSsK6oGnU2gDKWYsSyDORGePe7bnAFLKxsv3YKP4neqX/iCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T10:02:11.159541Z"},"content_sha256":"a2a81ccade6cef1c860b5995cbc8be13e2778ce4cb048e58aed8aeab6a17029b","schema_version":"1.0","event_id":"sha256:a2a81ccade6cef1c860b5995cbc8be13e2778ce4cb048e58aed8aeab6a17029b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LVVYWMGIRVWQU3USAZKXTLBFA3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Thinking Forward: Memory-Efficient Federated Finetuning of Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hui Guan, Kunjal Panchal, Lijun Zhang, Nisarg Parikh, Sunav Choudhary, Yuriy Brun","submitted_at":"2024-05-24T13:37:48Z","abstract_excerpt":"Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activations) for resource-constrained devices. While Forward-mode Auto-Differentiation (AD) can significantly reduce memory footprint from activations, we observe that directly applying it to LLM finetuning results in slow convergence and poor accuracy. In this paper, we introduce Spry, an FL algorithm t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15551","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/2405.15551/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":"2026-07-05T09:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t6UeIB4cnlaWzTKrz5qUYTmmCW4soQj+oFJAfpCNf7AQkZEMbEb9NEeVLmXZhzOm5CRn2Jb9vYZGvmLQP4kjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T10:02:11.159918Z"},"content_sha256":"90cf2481efdf67545e6ff61634f83b870a7ebd60aa81ea46588f71579ee4beac","schema_version":"1.0","event_id":"sha256:90cf2481efdf67545e6ff61634f83b870a7ebd60aa81ea46588f71579ee4beac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/bundle.json","state_url":"https://pith.science/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/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":"2026-08-21T10:02:11Z","links":{"resolver":"https://pith.science/pith/LVVYWMGIRVWQU3USAZKXTLBFA3","bundle":"https://pith.science/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/bundle.json","state":"https://pith.science/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LVVYWMGIRVWQU3USAZKXTLBFA3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LVVYWMGIRVWQU3USAZKXTLBFA3","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":"6ae78c0e62891412c33a56636aa8d10e5612d519ec6fbd03851dd0b10cb542be","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T13:37:48Z","title_canon_sha256":"162aaab58674268372bbbcbe3bae2ae98de89446b46f426a2aafd02cc0c312da"},"schema_version":"1.0","source":{"id":"2405.15551","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.15551","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2405.15551v2","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15551","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"LVVYWMGIRVWQ","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"LVVYWMGIRVWQU3US","created_at":"2026-07-05T09:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"LVVYWMGI","created_at":"2026-07-05T09:23:50Z"}],"graph_snapshots":[{"event_id":"sha256:90cf2481efdf67545e6ff61634f83b870a7ebd60aa81ea46588f71579ee4beac","target":"graph","created_at":"2026-07-05T09:23:50Z","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/2405.15551/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activations) for resource-constrained devices. While Forward-mode Auto-Differentiation (AD) can significantly reduce memory footprint from activations, we observe that directly applying it to LLM finetuning results in slow convergence and poor accuracy. In this paper, we introduce Spry, an FL algorithm t","authors_text":"Hui Guan, Kunjal Panchal, Lijun Zhang, Nisarg Parikh, Sunav Choudhary, Yuriy Brun","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T13:37:48Z","title":"Thinking Forward: Memory-Efficient Federated Finetuning of Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15551","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:a2a81ccade6cef1c860b5995cbc8be13e2778ce4cb048e58aed8aeab6a17029b","target":"record","created_at":"2026-07-05T09:23:50Z","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":"6ae78c0e62891412c33a56636aa8d10e5612d519ec6fbd03851dd0b10cb542be","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T13:37:48Z","title_canon_sha256":"162aaab58674268372bbbcbe3bae2ae98de89446b46f426a2aafd02cc0c312da"},"schema_version":"1.0","source":{"id":"2405.15551","kind":"arxiv","version":2}},"canonical_sha256":"5d6b8b30c88d6d0a6e92065579ac2506f379fa06d87d42440c5e7f1993f6c987","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d6b8b30c88d6d0a6e92065579ac2506f379fa06d87d42440c5e7f1993f6c987","first_computed_at":"2026-07-05T09:23:50.792722Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:50.792722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yx55K5WWbVooP2+nLHAJafGyK/dySPyaW8yoSdwk/eIFJJWbYWVgD7XN9QRyfskI4cEbCW4ytW5Z9TkR/t4HAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:50.793259Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.15551","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2a81ccade6cef1c860b5995cbc8be13e2778ce4cb048e58aed8aeab6a17029b","sha256:90cf2481efdf67545e6ff61634f83b870a7ebd60aa81ea46588f71579ee4beac"],"state_sha256":"1b56be749e6be77e25a739624a7fc8c433b74f8e61ac83b1c639842279b4a557"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tbHaCrhZkXpzzIy5VqsB7r2K4RlqpLhyBLa/aY+o7G7OC/hrlwmbfBGpcQmpi9Z4289IS3bteLGSBuE2kh/aCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T10:02:11.162473Z","bundle_sha256":"bbc2d4630f457442945acea3261a182f4155fd17891b688dcb0e3947613f3f06"}}