Robust Federated Learning With Double Denoising

Federated learning (FL), as a representative distributed learning paradigm, has achieved remarkable success. However, most existing FL studies assume that each client holds correctly labeled data, whereas in reality, noisy labels are ubiquitous on the client side. To mitigate the adverse impact of noisy labels on FL performance, we propose a robust FL method with double denoising. Specifically, we first perform primary denoising based on the idea of cross-prediction, where two global models are trained and mutually used on each client to identify and filter out mislabeled samples. Next, after fine-tuning the models, we develop secondary denoising by detecting and removing residual noisy samples through clustering based on loss values. In addition, we design a noise-tolerant local training strategy that dynamically assesses the influence of noisy data on local updates and applies differentiated update rules to prevent overfitting. Finally, experimental results on three benchmark datasets, including the real-world noisy dataset Clothing1M, demonstrate that our method effectively removes label noise, delivering improved performance of the global model.