Federated learning (FL), as a manner of distributed machine learning, is capable of significantly preserving clients' private data from being exposed to external adversaries.Nevertheless, private information can still be divulged by analyzing on the differences of uploaded parameters from clients, e.g., weights trained in deep neural networks.In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL).First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises.Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL.Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number N of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.Furthermore, we propose a K-random scheduling strategy, where K (1 < K < N ) clients are randomly selected from the N overall clients to participate in each aggregation.We also develop the corresponding convergence bound of the loss function in this case and the K-random scheduling strategy can also retain the above three properties.Moreover, we find that there is an optimal K that achieves the best convergence performance at a fixed privacy level.Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

Federated Learning With Differential Privacy: Algorithms and Performance Analysis