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cs.LG2026
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
Tian Wen, Zhiqin Yang, Yonggang Zhang +4
Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in dist…
cs.LG2024
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning
Xiuhua Lu, Peng Li, Xuefeng Jiang
Federated learning offers a paradigm to the challenge of preserving privacy in distributed machine learning. However, datasets distributed across each client in the real world are…