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To guarantee some form of\nrobustness, recent work suggests using variants of the geometric median as an\naggregation rule, in place of gradient averaging. Unfortunately, median-based\nrules can incur a prohibitive computational overhead in large-scale settings,\nand their convergence guarantees often require strong assumptions. In this\nwork, we present DRACO, a scalable framework for robust distributed training\nthat uses ideas from coding theory. In DRACO, each compute node evaluates\nredundant gradients that are used by the parameter server to eliminate the\neffects of adversarial updates. DRACO comes with problem-independent robustness\nguarantees, and the model that it trains is identical to the one trained in the\nadversary-free setup. We provide extensive experiments on real datasets and\ndistributed setups across a variety of large-scale models, where we show that\nDRACO is several times, to orders of magnitude faster than median-based\napproaches.","url_abs":"http://arxiv.org/abs/1803.09877v4","url_pdf":"http://arxiv.org/pdf/1803.09877v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"draco-byzantine-resilient-distributed","repo_url":"https://github.com/hwang595/Draco","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.09877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.09877"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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