[Paper] FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

Summary

FedSIR is a novel multi-stage framework designed to enhance the robustness of Federated Learning (FL) when faced with noisy labels from distributed clients. Unlike existing methods that rely on noise-tolerant loss functions or loss dynamics, FedSIR leverages the spectral structure of client features for identification and relabeling. This approach aims to significantly improve the performance and reliability of collaborative model training in real-world scenarios.

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