{"type":"ScholarlyArticle","title":"FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise","authors":[{"name":"Bixiao Zeng","slug":"bixiao-zeng","given":"Bixiao","family":"Zeng","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Xiaodong Yang","slug":"xiaodong-yang","given":"Xiaodong","family":"Yang","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Yiqiang Chen","slug":"yiqiang-chen","given":"Yiqiang","family":"Chen","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences; University of Chinese Academy of Sciences; Peng Cheng Laboratory"},{"name":"Zhiqi Shen","slug":"zhiqi-shen","given":"Zhiqi","family":"Shen","affiliation":"Nanyang Technological University"},{"name":"Hanchao Yu","slug":"hanchao-yu","given":"Hanchao","family":"Yu","affiliation":"Bureau of Frontier Sciences and Education , Chinese Academy of Sciences"},{"name":"Yingwei Zhang","slug":"yingwei-zhang","given":"Yingwei","family":"Zhang","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences"}],"abstract":"Federated learning (FL) facilitates collaborative model training across distributed clients while maintaining privacy. Federated noisy label learning (FNLL) is more of a challenge for data inaccessibility and noise heterogeneity. Existing works primarily assume clients are either noisy or clean, which may lack the flexibility to adapt to diverse label noise across different clients, especially when entirely clean or noisy clients are not the majority. To address this, we propose a general noise-robust federated learning framework called Federated Early-Stopping (FedES), which adaptively updates critical parameters of each local model based on their noise rates, thereby avoiding overfitting to noisy labels. FedES is composed of two stages: federated noise estimation and parameter-adaptive local updating \\& global aggregation. We introduce a signed distance based on local and global gradients during a federated round to estimate clients' noise rates without requiring additional information. Based on this measure, we employ various degrees of early-stopping during local updating on the clients, and further, a noise-aware global aggregation is employed to achieve noise-robust learning. Extensive experiments conducted on varying synthetic and real-world label noise demonstrate the superior performance of FedES over the state-of-the-art methods.","doi":"10.24963/ijcai.2024/599","url":"https://ijcai.org/proceedings/2024/599","pdf":"https://ijcai.org/proceedings/2024/0599.pdf","markdown":"https://ijcai.org/proceedings/2024/599.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/599","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":599,"pages":{"first":5416,"last":5424},"keywords":["Machine Learning: ML: Applications","Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Arpit et al., 2017] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. 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