{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:FVSZAJVLFKHWJ7AAKOBAANULMC","short_pith_number":"pith:FVSZAJVL","canonical_record":{"source":{"id":"1901.03040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T07:16:06Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"ca4c8dccc4a0053e68605371539d2582118aa3a04200f9423403bfc960a98ca8","abstract_canon_sha256":"8d95331a874124e7c88fb5d92ff52387641583e6cfc5542e2c2e18092ce72711"},"schema_version":"1.0"},"canonical_sha256":"2d659026ab2a8f64fc00538200368b6090755467aaa9baac62ef0605da76a155","source":{"kind":"arxiv","id":"1901.03040","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03040","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03040v1","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03040","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"pith_short_12","alias_value":"FVSZAJVLFKHW","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FVSZAJVLFKHWJ7AA","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FVSZAJVL","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:FVSZAJVLFKHWJ7AAKOBAANULMC","target":"record","payload":{"canonical_record":{"source":{"id":"1901.03040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T07:16:06Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"ca4c8dccc4a0053e68605371539d2582118aa3a04200f9423403bfc960a98ca8","abstract_canon_sha256":"8d95331a874124e7c88fb5d92ff52387641583e6cfc5542e2c2e18092ce72711"},"schema_version":"1.0"},"canonical_sha256":"2d659026ab2a8f64fc00538200368b6090755467aaa9baac62ef0605da76a155","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:35.719574Z","signature_b64":"kbDflqp6HKpYNive1PqmFpNgtxBSmzBsTqtVLVqKG/3pU2rSIbhbm6V7IsNXpRWRuZcsWY8NdRuc0cvul5HiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d659026ab2a8f64fc00538200368b6090755467aaa9baac62ef0605da76a155","last_reissued_at":"2026-05-17T23:56:35.718665Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:35.718665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.03040","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":"2026-05-17T23:56:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qk4psmQ6N1hOKRtATtFCobiSHTFcl29jvXvuq1XQ9fdpC8Q0EwtNVsgaPOmPm+CFtowMKk6BXIDZ/BmH30daDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T01:13:27.575048Z"},"content_sha256":"6a37c850a3dea9da2b1d870d7b97dd1a72f7568434ea80afcb7ebae06f8a0bef","schema_version":"1.0","event_id":"sha256:6a37c850a3dea9da2b1d870d7b97dd1a72f7568434ea80afcb7ebae06f8a0bef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:FVSZAJVLFKHWJ7AAKOBAANULMC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantized Epoch-SGD for Communication-Efficient Distributed Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hao Gao, Shen-Yi Zhao, Wu-Jun Li","submitted_at":"2019-01-10T07:16:06Z","abstract_excerpt":"Due to its efficiency and ease to implement, stochastic gradient descent (SGD) has been widely used in machine learning. In particular, SGD is one of the most popular optimization methods for distributed learning. Recently, quantized SGD (QSGD), which adopts quantization to reduce the communication cost in SGD-based distributed learning, has attracted much attention. Although several QSGD methods have been proposed, some of them are heuristic without theoretical guarantee, and others have high quantization variance which makes the convergence become slow. In this paper, we propose a new method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03040","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":""},"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":"2026-05-17T23:56:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SLTggYx0vuTwKI4mw5NEfjn+0p28W1qmgi2GhHrei0XKv3EUq9eb3EmgwRjrHeJo+YEOCadX2h7aK3xjpUIBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T01:13:27.575422Z"},"content_sha256":"a717f7fcad66a196582743f18cbd2dbc846cd9084c85340c0382426c0c2b1ea2","schema_version":"1.0","event_id":"sha256:a717f7fcad66a196582743f18cbd2dbc846cd9084c85340c0382426c0c2b1ea2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/bundle.json","state_url":"https://pith.science/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/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":"2026-06-28T01:13:27Z","links":{"resolver":"https://pith.science/pith/FVSZAJVLFKHWJ7AAKOBAANULMC","bundle":"https://pith.science/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/bundle.json","state":"https://pith.science/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FVSZAJVLFKHWJ7AAKOBAANULMC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FVSZAJVLFKHWJ7AAKOBAANULMC","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":"8d95331a874124e7c88fb5d92ff52387641583e6cfc5542e2c2e18092ce72711","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T07:16:06Z","title_canon_sha256":"ca4c8dccc4a0053e68605371539d2582118aa3a04200f9423403bfc960a98ca8"},"schema_version":"1.0","source":{"id":"1901.03040","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03040","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03040v1","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03040","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"pith_short_12","alias_value":"FVSZAJVLFKHW","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FVSZAJVLFKHWJ7AA","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FVSZAJVL","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:a717f7fcad66a196582743f18cbd2dbc846cd9084c85340c0382426c0c2b1ea2","target":"graph","created_at":"2026-05-17T23:56:35Z","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"},"paper":{"abstract_excerpt":"Due to its efficiency and ease to implement, stochastic gradient descent (SGD) has been widely used in machine learning. In particular, SGD is one of the most popular optimization methods for distributed learning. Recently, quantized SGD (QSGD), which adopts quantization to reduce the communication cost in SGD-based distributed learning, has attracted much attention. Although several QSGD methods have been proposed, some of them are heuristic without theoretical guarantee, and others have high quantization variance which makes the convergence become slow. In this paper, we propose a new method","authors_text":"Hao Gao, Shen-Yi Zhao, Wu-Jun Li","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T07:16:06Z","title":"Quantized Epoch-SGD for Communication-Efficient Distributed Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03040","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":"sha256:6a37c850a3dea9da2b1d870d7b97dd1a72f7568434ea80afcb7ebae06f8a0bef","target":"record","created_at":"2026-05-17T23:56:35Z","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":"8d95331a874124e7c88fb5d92ff52387641583e6cfc5542e2c2e18092ce72711","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T07:16:06Z","title_canon_sha256":"ca4c8dccc4a0053e68605371539d2582118aa3a04200f9423403bfc960a98ca8"},"schema_version":"1.0","source":{"id":"1901.03040","kind":"arxiv","version":1}},"canonical_sha256":"2d659026ab2a8f64fc00538200368b6090755467aaa9baac62ef0605da76a155","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2d659026ab2a8f64fc00538200368b6090755467aaa9baac62ef0605da76a155","first_computed_at":"2026-05-17T23:56:35.718665Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:35.718665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kbDflqp6HKpYNive1PqmFpNgtxBSmzBsTqtVLVqKG/3pU2rSIbhbm6V7IsNXpRWRuZcsWY8NdRuc0cvul5HiCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:35.719574Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.03040","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a37c850a3dea9da2b1d870d7b97dd1a72f7568434ea80afcb7ebae06f8a0bef","sha256:a717f7fcad66a196582743f18cbd2dbc846cd9084c85340c0382426c0c2b1ea2"],"state_sha256":"05669491a0389eb9c091c5d6b2907c7bb96a8e268c0cbe8a1e94c62f37e14c19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wP5OQ5C9/Cf6AftG2AAkaNvf4hEE+n9i0wwAZzbQRPdxMQT6Df3i7FDjgXmmA5J0Ez1eKOa2x/p08NIegRDdCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T01:13:27.577306Z","bundle_sha256":"37cfb4154d0c436a75fc7acba14c9f90ebd3f501c94fa4df68a172ade0b2dc3d"}}