FedGP: Correlation-Based Active Client Selection for Heterogeneous Federated Learning

Client-wise heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may differ dramatically, the client selection strategy can largely influence the convergence rate of the FL process. Several recent studies adopt active client selection strategies. However, they neglect the loss correlations between the clients and achieve marginal improvement compared to the uniform selection strategy. In this work, we propose FedGP -- a federated learning framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). To make the GP training feasible in the communication-bounded FL process, we develop a GP training method utilizing the historical samples efficiently to reduce the communication cost. Finally, based on the correlations we learned, we derive the client selection with an enlarged reduction of expected global loss in each round. Our experimental results show that compared to the latest active client selection strategy, FedGP can improve the convergence rates by $1.3\sim2.3\times$ and $1.2\sim1.4\times$ on FMNIST and CIFAR-10, respectively.