A Client Selection Method Based on Loss Function Optimization for Federated Learning

Abstract

Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the global model with the selected local models. We propose FedChoice, a client selection method based on loss function optimization, to select appropriate local models to improve the convergence of the global model. It firstly sets selected probability for clients with the value of loss function, and the client with high loss will be set higher selected probability, which can make them more likely to participate in training. Then, it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction, respectively, and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviation of the local gradient caused by the Non-IID data. We make experiments to verify the validity of FedChoice on CIFAR-10, CINIC-10, MNIST, EMNITS, and FEMNIST datasets, and the results show that the convergence of FedChoice is significantly improved, compared with FedAvg, FedProx, and FedNova.