{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/weight-for-robustness-a-comprehensive","title":"Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML","arxiv_id":"2501.09621","date":"2025-01-16","proceeding":null,"authors":["Tehila Dahan","Kfir Y. Levy"],"abstract":"We address the challenges of Byzantine-robust training in asynchronous distributed machine learning systems, aiming to enhance efficiency amid massive parallelization and heterogeneous computing resources. Asynchronous systems, marked by independently operating workers and intermittent updates, uniquely struggle with maintaining integrity against Byzantine failures, which encompass malicious or erroneous actions that disrupt learning. The inherent delays in such settings not only introduce additional bias to the system but also obscure the disruptions caused by Byzantine faults. To tackle these issues, we adapt the Byzantine framework to asynchronous dynamics by introducing a novel weighted robust aggregation framework. This allows for the extension of robust aggregators and a recent meta-aggregator to their weighted versions, mitigating the effects of delayed updates. By further incorporating a recent variance-reduction technique, we achieve an optimal convergence rate for the first time in an asynchronous Byzantine environment. Our methodology is rigorously validated through empirical and theoretical analysis, demonstrating its effectiveness in enhancing fault tolerance and optimizing performance in asynchronous ML systems.","url_abs":"https://arxiv.org/abs/2501.09621v1","url_pdf":"https://arxiv.org/pdf/2501.09621v1.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":"weight-for-robustness-a-comprehensive","repo_url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml","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=2501.09621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09621"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"d50472c73c35e157","entry":"filter_valid_args","repo":"dahan198/asynchronous-fault-tolerant-ml","repo_kind":"official","path":"weight4robustness/utils.py","file_url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml/blob/HEAD/weight4robustness/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d50472c73c35e157"}},{"code_sha256_prefix":"ff1a140d2112b5e6","entry":"sample_worker_by_id","repo":"dahan198/asynchronous-fault-tolerant-ml","repo_kind":"official","path":"weight4robustness/worker_sampler.py","file_url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml/blob/HEAD/weight4robustness/worker_sampler.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ff1a140d2112b5e6"}},{"code_sha256_prefix":"7828882841edd091","entry":"sample_worker_by_id_square","repo":"dahan198/asynchronous-fault-tolerant-ml","repo_kind":"official","path":"weight4robustness/worker_sampler.py","file_url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml/blob/HEAD/weight4robustness/worker_sampler.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7828882841edd091"}},{"code_sha256_prefix":"502566df6a6fceca","entry":"uniform_sample_worker","repo":"dahan198/asynchronous-fault-tolerant-ml","repo_kind":"official","path":"weight4robustness/worker_sampler.py","file_url":"https://github.com/dahan198/asynchronous-fault-tolerant-ml/blob/HEAD/weight4robustness/worker_sampler.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"502566df6a6fceca"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}