The advent of federated learning (FL) has presented a viable solution for distributed training in edge environment, while simultaneously ensuring the preservation of privacy. In real-world scenarios, edge devices may be subject to label noise caused by environmental differences, automated weakly supervised annotation, malicious tampering, or even human error. However, the potential of the noisy samples have not been fully leveraged by prior studies on FL aimed at addressing label noise. Rather, they have primarily focused on conventional filtering or correction techniques to alleviate the impact of noisy labels. To tackle this challenge, a method, named DETECTION, is proposed in this article. It aims at effectively detecting noisy clients and mitigating the adverse impact of label noise while preserving data privacy. Specially, a confidence scoring mechanism based on local intrinsic dimensionality (LID) is investigated for distinguishing noisy clients from clean clients. Then, a loss function based on prototype contrastive learning is designed to optimize the local model. To address the varying levels of noise across clients, a LID weighted aggregation strategy (LA) is introduced. Experimental results on three datasets demonstrate the effectiveness of DETECTION in addressing the issue of label noise in FL while maintaining data privacy.
