{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:25VEYXW46WHXJQR3AEJKO563EV","short_pith_number":"pith:25VEYXW4","canonical_record":{"source":{"id":"2209.06359","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-14T00:48:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d107a5542ae58680266d3bf71abf77bf822286b60b368546bcfe15bf3a78f7ef","abstract_canon_sha256":"8c353e96e8bac0350fb16f84e3585641b715b5d1450bafadac974712735fb2b4"},"schema_version":"1.0"},"canonical_sha256":"d76a4c5edcf58f74c23b0112a777db25450245dd56e7478ae66250498dab0cd9","source":{"kind":"arxiv","id":"2209.06359","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.06359","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"arxiv_version","alias_value":"2209.06359v1","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.06359","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_12","alias_value":"25VEYXW46WHX","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_16","alias_value":"25VEYXW46WHXJQR3","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_8","alias_value":"25VEYXW4","created_at":"2026-07-05T04:57:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:25VEYXW46WHXJQR3AEJKO563EV","target":"record","payload":{"canonical_record":{"source":{"id":"2209.06359","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-14T00:48:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d107a5542ae58680266d3bf71abf77bf822286b60b368546bcfe15bf3a78f7ef","abstract_canon_sha256":"8c353e96e8bac0350fb16f84e3585641b715b5d1450bafadac974712735fb2b4"},"schema_version":"1.0"},"canonical_sha256":"d76a4c5edcf58f74c23b0112a777db25450245dd56e7478ae66250498dab0cd9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:57:29.713944Z","signature_b64":"7sR+UUc4J07YpAzTrr+9tfYXzYgEcPk4YxcfggO/BwjFDsn1ppUE0DtS4rntx2j+dVm0yG1oI0C4ubLgKSkLBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d76a4c5edcf58f74c23b0112a777db25450245dd56e7478ae66250498dab0cd9","last_reissued_at":"2026-07-05T04:57:29.713584Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:57:29.713584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.06359","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:57:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OoNIzLybAhGy9tdBAGFGqQ8AoWDHTxDEz1Np7UPNIhO3AAWvTFwHorSZF8yjj2virBy9ZqQmMpx6cu/TgtfVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:16:59.132129Z"},"content_sha256":"cdd8970c86101bf491a3611c506d6773184d106dc3fc800bfed04233efb8ccf3","schema_version":"1.0","event_id":"sha256:cdd8970c86101bf491a3611c506d6773184d106dc3fc800bfed04233efb8ccf3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:25VEYXW46WHXJQR3AEJKO563EV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Pruning: Improving Neural Network Efficiency with Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ding Zhao, Fran\\c{c}oise Beaufays, Giovanni Motta, Li Xiong, Rongmei Lin, Tien-Ju Yang, Yonghui Xiao","submitted_at":"2022-09-14T00:48:37Z","abstract_excerpt":"Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been widely used and is considered to be an effective decentralized technique by collaboratively learning a shared prediction model while keeping the data local on different clients devices. However, the limited computation and communication resources on clients devices present practical difficulties for large models. To overcome such challenges, we propose Federated Pruning to train a reduced model under the federated set"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.06359","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/2209.06359/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":"2026-07-05T04:57:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GRTH+woSqJ36tDZ7yEzlzKNa5frSBfyTrea/LgPf4DCmqY/wYZn7n+qWRMuaDxwwSSHY32KBvbL7XWKxAZoTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:16:59.132515Z"},"content_sha256":"f29753ebbd503ed6847634238c97fee14c98692d7faedbc1ba6a110dd793a342","schema_version":"1.0","event_id":"sha256:f29753ebbd503ed6847634238c97fee14c98692d7faedbc1ba6a110dd793a342"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/25VEYXW46WHXJQR3AEJKO563EV/bundle.json","state_url":"https://pith.science/pith/25VEYXW46WHXJQR3AEJKO563EV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/25VEYXW46WHXJQR3AEJKO563EV/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":"2026-08-05T21:16:59Z","links":{"resolver":"https://pith.science/pith/25VEYXW46WHXJQR3AEJKO563EV","bundle":"https://pith.science/pith/25VEYXW46WHXJQR3AEJKO563EV/bundle.json","state":"https://pith.science/pith/25VEYXW46WHXJQR3AEJKO563EV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/25VEYXW46WHXJQR3AEJKO563EV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:25VEYXW46WHXJQR3AEJKO563EV","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":"8c353e96e8bac0350fb16f84e3585641b715b5d1450bafadac974712735fb2b4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-14T00:48:37Z","title_canon_sha256":"d107a5542ae58680266d3bf71abf77bf822286b60b368546bcfe15bf3a78f7ef"},"schema_version":"1.0","source":{"id":"2209.06359","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.06359","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"arxiv_version","alias_value":"2209.06359v1","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.06359","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_12","alias_value":"25VEYXW46WHX","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_16","alias_value":"25VEYXW46WHXJQR3","created_at":"2026-07-05T04:57:29Z"},{"alias_kind":"pith_short_8","alias_value":"25VEYXW4","created_at":"2026-07-05T04:57:29Z"}],"graph_snapshots":[{"event_id":"sha256:f29753ebbd503ed6847634238c97fee14c98692d7faedbc1ba6a110dd793a342","target":"graph","created_at":"2026-07-05T04:57:29Z","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/2209.06359/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been widely used and is considered to be an effective decentralized technique by collaboratively learning a shared prediction model while keeping the data local on different clients devices. However, the limited computation and communication resources on clients devices present practical difficulties for large models. To overcome such challenges, we propose Federated Pruning to train a reduced model under the federated set","authors_text":"Ding Zhao, Fran\\c{c}oise Beaufays, Giovanni Motta, Li Xiong, Rongmei Lin, Tien-Ju Yang, Yonghui Xiao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-14T00:48:37Z","title":"Federated Pruning: Improving Neural Network Efficiency with Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.06359","kind":"arxiv","version":1},"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:cdd8970c86101bf491a3611c506d6773184d106dc3fc800bfed04233efb8ccf3","target":"record","created_at":"2026-07-05T04:57:29Z","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":"8c353e96e8bac0350fb16f84e3585641b715b5d1450bafadac974712735fb2b4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-14T00:48:37Z","title_canon_sha256":"d107a5542ae58680266d3bf71abf77bf822286b60b368546bcfe15bf3a78f7ef"},"schema_version":"1.0","source":{"id":"2209.06359","kind":"arxiv","version":1}},"canonical_sha256":"d76a4c5edcf58f74c23b0112a777db25450245dd56e7478ae66250498dab0cd9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d76a4c5edcf58f74c23b0112a777db25450245dd56e7478ae66250498dab0cd9","first_computed_at":"2026-07-05T04:57:29.713584Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:57:29.713584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7sR+UUc4J07YpAzTrr+9tfYXzYgEcPk4YxcfggO/BwjFDsn1ppUE0DtS4rntx2j+dVm0yG1oI0C4ubLgKSkLBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:57:29.713944Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.06359","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cdd8970c86101bf491a3611c506d6773184d106dc3fc800bfed04233efb8ccf3","sha256:f29753ebbd503ed6847634238c97fee14c98692d7faedbc1ba6a110dd793a342"],"state_sha256":"92b262199b5f0dda9c50d535b357a72599653f3201d5dc0f78aef1153f047ade"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j0EJCt0lBho5XN5xi3BUIXesc9AwJ2JU2DbQGEoy7zS+LDRXIp/slCNfOZxftMChf3BSyfVHWbZ3sVBpFwavBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T21:16:59.134896Z","bundle_sha256":"e43e5044277d17fabbd9aae20e216127c78b5ad58e8cbd1b85f27c63d951d705"}}