FedDC: Label Noise Correction With Dynamic Clients for Federated Learning

Federated learning (FL) is a distributed machine learning training paradigm that protects user privacy by training with user data stored on a local device called a client. In realworld FL systems, the number of clients often varies due to both external and internal factors. New clients may join the system right during the training process. Moreover, existing and new clients can have noisy labels with varying levels of noise. Although some works have been proposed to solve the noise problem in FL with the clients that are fixed and determined prior. These methods have not considered the case when new clients join. Therefore, there is a need for a framework that can handle dynamically noisy clients. In this article, we introduce a new framework named FedDC to handle noisy labels in FL systems with dynamic clients. Our FedDC framework is built on top of the 3-stage FedCorr framework which has been designed to work with a fixed number of clients. In our framework, existing noisy clients will be identified through local intrinsic dimensionality (LID) scores. Then to identify new noisy clients, we use a loss threshold combined with the LID scoring technique in the first stage and with only the loss threshold in the second stage. Our experiments on three benchmark datasets that are CIFAR-10 and CIFAR-100 with independent and identically distributed/nonindependent and nonidentically distributed data partition and a real-world noisy dataset, Clothing1M, demonstrate that FedDC helps mitigate the negative impact of new noisy clients and achieves outperformed accuracy compared to FedCorr. Our code is made available at: https://github.com/gttrung/FedDC