{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:TTR2CNVCN6C4GV2ECGRZXKELA2","short_pith_number":"pith:TTR2CNVC","canonical_record":{"source":{"id":"2305.18469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T09:02:05Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"117145bcfb0a51463434f41fee1c3b19d137499167041ce64ee94686a5397138","abstract_canon_sha256":"f72f7ee2719c745029dd6b982ffa4d0793617553069d78896801aeb8127ec527"},"schema_version":"1.0"},"canonical_sha256":"9ce3a136a26f85c3574411a39ba88b069f0beccbd694a84b1ecce52482f8abe1","source":{"kind":"arxiv","id":"2305.18469","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.18469","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2305.18469v1","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18469","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"TTR2CNVCN6C4","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"TTR2CNVCN6C4GV2E","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"TTR2CNVC","created_at":"2026-07-05T07:36:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:TTR2CNVCN6C4GV2ECGRZXKELA2","target":"record","payload":{"canonical_record":{"source":{"id":"2305.18469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T09:02:05Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"117145bcfb0a51463434f41fee1c3b19d137499167041ce64ee94686a5397138","abstract_canon_sha256":"f72f7ee2719c745029dd6b982ffa4d0793617553069d78896801aeb8127ec527"},"schema_version":"1.0"},"canonical_sha256":"9ce3a136a26f85c3574411a39ba88b069f0beccbd694a84b1ecce52482f8abe1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:51.499388Z","signature_b64":"kkp2/rWYN1t/vI4nXYsh8WhXgEKa4CGSlhxio9MfWzs6bPuuSZ7wi16tpnN3u2nzcR7tk7GHRKKSTRzNb2SRCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ce3a136a26f85c3574411a39ba88b069f0beccbd694a84b1ecce52482f8abe1","last_reissued_at":"2026-07-05T07:36:51.498952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:51.498952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.18469","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":"2026-07-05T07:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PpFydJdSycqvt77MBLxXRaWfYi5SO35jBZ/iZlw6ypQCBLwL8YTZVuMFvcU0I2K+CGbSmolvhHNydgsXtKruBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T09:52:26.085972Z"},"content_sha256":"3ab49d267df4cb15f9ce21250ef8e52030a938d4cdb6b47d934fbc6696893cce","schema_version":"1.0","event_id":"sha256:3ab49d267df4cb15f9ce21250ef8e52030a938d4cdb6b47d934fbc6696893cce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:TTR2CNVCN6C4GV2ECGRZXKELA2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reducing Communication for Split Learning by Randomized Top-k Sparsification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Binhui Yao, Chaochao Chen, Fei Zheng, Lingjuan Lyu","submitted_at":"2023-05-29T09:02:05Z","abstract_excerpt":"Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18469","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/2305.18469/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":"2026-07-05T07:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Adkityr1xIjRzsXb8HLyT2qu8rcmUCWrnRYSi0Z/LUh00P4SOW71iMNvCossfrA+Y+B9torv4jWP4lWrdiw7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T09:52:26.086507Z"},"content_sha256":"92fc08c1d81e4c7ce824edeeab69c13f24b67a1d58abad3f9f5630aa083714fe","schema_version":"1.0","event_id":"sha256:92fc08c1d81e4c7ce824edeeab69c13f24b67a1d58abad3f9f5630aa083714fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/bundle.json","state_url":"https://pith.science/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/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":"2026-08-01T09:52:26Z","links":{"resolver":"https://pith.science/pith/TTR2CNVCN6C4GV2ECGRZXKELA2","bundle":"https://pith.science/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/bundle.json","state":"https://pith.science/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TTR2CNVCN6C4GV2ECGRZXKELA2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TTR2CNVCN6C4GV2ECGRZXKELA2","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":"f72f7ee2719c745029dd6b982ffa4d0793617553069d78896801aeb8127ec527","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T09:02:05Z","title_canon_sha256":"117145bcfb0a51463434f41fee1c3b19d137499167041ce64ee94686a5397138"},"schema_version":"1.0","source":{"id":"2305.18469","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.18469","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2305.18469v1","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18469","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"TTR2CNVCN6C4","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"TTR2CNVCN6C4GV2E","created_at":"2026-07-05T07:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"TTR2CNVC","created_at":"2026-07-05T07:36:51Z"}],"graph_snapshots":[{"event_id":"sha256:92fc08c1d81e4c7ce824edeeab69c13f24b67a1d58abad3f9f5630aa083714fe","target":"graph","created_at":"2026-07-05T07:36:51Z","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/2305.18469/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model genera","authors_text":"Binhui Yao, Chaochao Chen, Fei Zheng, Lingjuan Lyu","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T09:02:05Z","title":"Reducing Communication for Split Learning by Randomized Top-k Sparsification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18469","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":"sha256:3ab49d267df4cb15f9ce21250ef8e52030a938d4cdb6b47d934fbc6696893cce","target":"record","created_at":"2026-07-05T07:36:51Z","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":"f72f7ee2719c745029dd6b982ffa4d0793617553069d78896801aeb8127ec527","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T09:02:05Z","title_canon_sha256":"117145bcfb0a51463434f41fee1c3b19d137499167041ce64ee94686a5397138"},"schema_version":"1.0","source":{"id":"2305.18469","kind":"arxiv","version":1}},"canonical_sha256":"9ce3a136a26f85c3574411a39ba88b069f0beccbd694a84b1ecce52482f8abe1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ce3a136a26f85c3574411a39ba88b069f0beccbd694a84b1ecce52482f8abe1","first_computed_at":"2026-07-05T07:36:51.498952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:36:51.498952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kkp2/rWYN1t/vI4nXYsh8WhXgEKa4CGSlhxio9MfWzs6bPuuSZ7wi16tpnN3u2nzcR7tk7GHRKKSTRzNb2SRCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:36:51.499388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.18469","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ab49d267df4cb15f9ce21250ef8e52030a938d4cdb6b47d934fbc6696893cce","sha256:92fc08c1d81e4c7ce824edeeab69c13f24b67a1d58abad3f9f5630aa083714fe"],"state_sha256":"99a6fc2d3347ecf3455818ab1507d6878cf38ef79c91ae6828c6d9e9468bab51"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5FIsC2N15Yjd6uMCUz52H7PECbxwvFjZVTjgtepgHuuCpleZth74oARSs8M4LnAuXn4+3oP62n1L+BW+V8F1Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T09:52:26.091891Z","bundle_sha256":"13689e926f2330b840fb24996bd28534cf40202170a0eb259992a20d7b7fb37b"}}