Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee
Xiuhua Wang, Shuai Wang, Yiwei Li, Fengrui Fan, Shikang Li, Xiaodong Sheldon Lin · IEEE Transactions on Artificial Intelligence · 2024
Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients’ private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients’ privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, namedDP-FedCVRto mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposedDP-FedCVRalgorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate thatDP-FedCVRprovides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets.