{"type":"ScholarlyArticle","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","authors":[{"name":"Nannan Wu","slug":"nannan-wu","given":"Nannan","family":"Wu","affiliation":"School of Electronic Information and Communications , Huazhong University of Science and Technology"},{"name":"Li Yu","slug":"li-yu","given":"Li","family":"Yu","affiliation":"School of Electronic Information and Communications , Huazhong University of Science and Technology"},{"name":"Xuefeng Jiang","slug":"xuefeng-jiang","given":"Xuefeng","family":"Jiang","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Kwang-Ting Cheng","slug":"kwang-ting-cheng","given":"Kwang-Ting","family":"Cheng","affiliation":"School of Engineering , Hong Kong University of Science and Technology"},{"name":"Zengqiang Yan","slug":"zengqiang-yan","given":"Zengqiang","family":"Yan","affiliation":"School of Electronic Information and Communications , Huazhong University of Science and Technology"}],"abstract":"Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated label noise, especially in medical scenarios. In this paper, we first formulate a new and more realistic federated label noise problem where global data is class-imbalanced and label noise is heterogeneous, and then propose a two-stage framework named FedNoRo for noise-robust federated learning. Specifically, in the first stage of FedNoRo, per-class loss indicators followed by Gaussian Mixture Model are deployed for noisy client identification. In the second stage, knowledge distillation and a distance-aware aggregation function are jointly adopted for noise-robust federated model updating. Experimental results on the widely-used ICH and ISIC2019 datasets demonstrate the superiority of FedNoRo against the state-of-the-art FNLL methods for addressing class imbalance and label noise heterogeneity in real-world FL scenarios.","doi":"10.24963/ijcai.2023/492","url":"https://ijcai.org/proceedings/2023/492","pdf":"https://ijcai.org/proceedings/2023/0492.pdf","markdown":"https://ijcai.org/proceedings/2023/492.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/492","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":492,"pages":{"first":4424,"last":4432},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Classification","Machine Learning: ML: Robustness"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2018] Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Bren- dan McMahan. cpSGD: Communication-efficient and differentially-private distributed sgd. 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