{"type":"ScholarlyArticle","title":"Mean Aggregator Is More Robust than Robust Aggregators under Label Poisoning Attacks","authors":[{"name":"Jie Peng","slug":"jie-peng","given":"Jie","family":"Peng","affiliation":"Sun Yat-Sen University"},{"name":"Weiyu Li","slug":"weiyu-li","given":"Weiyu","family":"Li","affiliation":"Harvard University"},{"name":"Qing Ling","slug":"qing-ling","given":"Qing","family":"Ling","affiliation":"Sun Yat-Sen University"}],"abstract":"Robustness to malicious attacks is of paramount importance for distributed learning. Existing works often consider the classical Byzantine attacks model, which assumes that some workers can send arbitrarily malicious messages to the server and disturb the aggregation steps of the distributed learning process. To defend against such worst-case Byzantine attacks, various robust aggregators have been proven effective and much superior to the often-used mean aggregator. In this paper, we show that robust aggregators are too conservative for a class of weak but practical malicious attacks, as known as label poisoning attacks, where the sample labels of some workers are poisoned. Surprisingly, we are able to show that the mean aggregator is more robust than the state-of-the-art robust aggregators in theory, given that the distributed data are sufficiently heterogeneous. In fact, the learning error of the mean aggregator is proven to be optimal in order. Experimental results corroborate our theoretical findings, demonstrating the superiority of the mean aggregator under label poisoning attacks.","doi":"10.24963/ijcai.2024/530","url":"https://ijcai.org/proceedings/2024/530","pdf":"https://ijcai.org/proceedings/2024/0530.pdf","markdown":"https://ijcai.org/proceedings/2024/530.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/530","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":530,"pages":{"first":4797,"last":4805},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Optimization"],"videos":[],"references":[{"position":1,"raw":"[Allouah et al., 2023] Youssef Allouah, Sadegh Farhad- khani, Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, and John Stephan. 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