Harnessing Feature Distribution Consistency for Federated Learning with Noisy Labels

Label noise in federated learning (FL) is a significant detrimental factor that substantially degrades FL performance. Current methods attempt to mitigate this issue by identifying noisy-labeled samples using rudimentary indicators. However, these indicators fail to distinguish between clean and noisy labels in heavily poisoned scenarios. Although global assessment has been introduced to enhance the recognition of noisy labels, existing methods still struggle to prevent performance degradation in FL systems. Therefore, this paper proposes leveraging the global feature distribution (an integration of local distributions) to improve the detection and correction of noisy labels in FL systems. Specifically, we assess the consistency between the category to which samples belong in the global distribution and their labels to detect noisy labels. Subsequently, a temporal dual-view consistency (TDC) mechanism is designed and introduced to correct the detected noisy labels. TDC evaluates label consistency from the perspectives of sample diversity and label continuity, thereby enhancing the reliability of label correction. Extensive experimental results on both synthetic and real-world noisy datasets demonstrate that the proposed method surpasses current SOTA approaches.