{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7LQATNQSOGIITAWVUC7DDWU7ZH","short_pith_number":"pith:7LQATNQS","schema_version":"1.0","canonical_sha256":"fae009b61271908982d5a0be31da9fc9f0498534178c61212fb6c7c00e7fe2fb","source":{"kind":"arxiv","id":"2506.17974","version":1},"attestation_state":"computed","paper":{"title":"Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Caesar Wu, Hongyang Li, Lincen Bai, Mohammed Chadli, Pascal Bouvry, Said Mammar","submitted_at":"2025-06-22T10:21:49Z","abstract_excerpt":"We propose LQ-SGD (Low-Rank Quantized Stochastic Gradient Descent), an efficient communication gradient compression algorithm designed for distributed training. LQ-SGD further develops on the basis of PowerSGD by incorporating the low-rank approximation and log-quantization techniques, which drastically reduce the communication overhead, while still ensuring the convergence speed of training and model accuracy. In addition, LQ-SGD and other compression-based methods show stronger resistance to gradient inversion than traditional SGD, providing a more robust and efficient optimization path for "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.17974","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T10:21:49Z","cross_cats_sorted":[],"title_canon_sha256":"a854fe6cd28f78893a725b35f30bb898eaa6f8538efd9976a4870f18535d374a","abstract_canon_sha256":"db84097a17bbd9198c8a783969f451fcaa6cac2cceadba83e6e3f76830e67884"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:26.402038Z","signature_b64":"4rDjGCkEjoKJTZNpVNrusGqWTMJpFZIp5/pQamehDtrvzsAIWvOx4L7naOpR5Ifaxmek5EgwJ+Yw+1z8r5rrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fae009b61271908982d5a0be31da9fc9f0498534178c61212fb6c7c00e7fe2fb","last_reissued_at":"2026-07-05T11:25:26.401519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:26.401519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Caesar Wu, Hongyang Li, Lincen Bai, Mohammed Chadli, Pascal Bouvry, Said Mammar","submitted_at":"2025-06-22T10:21:49Z","abstract_excerpt":"We propose LQ-SGD (Low-Rank Quantized Stochastic Gradient Descent), an efficient communication gradient compression algorithm designed for distributed training. LQ-SGD further develops on the basis of PowerSGD by incorporating the low-rank approximation and log-quantization techniques, which drastically reduce the communication overhead, while still ensuring the convergence speed of training and model accuracy. In addition, LQ-SGD and other compression-based methods show stronger resistance to gradient inversion than traditional SGD, providing a more robust and efficient optimization path for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17974","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/2506.17974/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.17974","created_at":"2026-07-05T11:25:26.401580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17974v1","created_at":"2026-07-05T11:25:26.401580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17974","created_at":"2026-07-05T11:25:26.401580+00:00"},{"alias_kind":"pith_short_12","alias_value":"7LQATNQSOGII","created_at":"2026-07-05T11:25:26.401580+00:00"},{"alias_kind":"pith_short_16","alias_value":"7LQATNQSOGIITAWV","created_at":"2026-07-05T11:25:26.401580+00:00"},{"alias_kind":"pith_short_8","alias_value":"7LQATNQS","created_at":"2026-07-05T11:25:26.401580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH","json":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH.json","graph_json":"https://pith.science/api/pith-number/7LQATNQSOGIITAWVUC7DDWU7ZH/graph.json","events_json":"https://pith.science/api/pith-number/7LQATNQSOGIITAWVUC7DDWU7ZH/events.json","paper":"https://pith.science/paper/7LQATNQS"},"agent_actions":{"view_html":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH","download_json":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH.json","view_paper":"https://pith.science/paper/7LQATNQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17974&json=true","fetch_graph":"https://pith.science/api/pith-number/7LQATNQSOGIITAWVUC7DDWU7ZH/graph.json","fetch_events":"https://pith.science/api/pith-number/7LQATNQSOGIITAWVUC7DDWU7ZH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH/action/storage_attestation","attest_author":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH/action/author_attestation","sign_citation":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH/action/citation_signature","submit_replication":"https://pith.science/pith/7LQATNQSOGIITAWVUC7DDWU7ZH/action/replication_record"}},"created_at":"2026-07-05T11:25:26.401580+00:00","updated_at":"2026-07-05T11:25:26.401580+00:00"}