Historical Self-Repair for Federated Learning with Delayed Detection of Noisy Labels

Federated learning with noisy labels aims to train robust global models in federated settings where clients may hold noisy labels. Most existing methods follow a detection-and-correction paradigm: they identify noisy clients or samples during training and correct the detected noise through label correction, filtering, or robust optimization. However, this paradigm faces the problem of delayed detection, where some noisy samples are detected after multiple communication rounds. Although these samples can be corrected after being detected, their updates have already affected the global model during the earlier training step, leaving harmful historical influence on subsequent optimization. To address this problem, we propose FedHSR, a historical self-repair framework for robust model training. FedHSR maintains the global training trajectory and repairs the affected historical trajectory through two modules. Historical Replay reconstructs the historical updates of noisy clients based on the detected data, while Historical Response Calibration estimates the responses of clean clients to the model shift induced by historical repair without requiring them to repeat their historical training. We further establish theoretical bounds for both response estimation and trajectory repair. Experiments show that FedHSR effectively repairs the affected trajectory and consistently improves the performance of existing methods. Code is available at https://anonymous.4open.science/r/ICLR-59567.