Privacy amplification for wireless federated learning with Rényi differential privacy and subsampling

Abstract

A key issue in current federated learning research is how to improve the performance of federated learning algorithms by reducing communication overhead and computing costs while ensuring data privacy. This paper proposed an efficient wireless transmission scheme termed the subsampling privacy-enabled RDP wireless transmission system (SS-RDP-WTS), which can reduce the communication and computing overhead in the process of learning but also enhance the privacy protection ability of federated learning. We proved our scheme's convergence and analyzed its privacy guarantee, as well as demoonstrated the performance of our scheme on the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research, 10 classes datasets (CIFAR10).

Keywords

• subsampling
• rényi differential privacy
• federated learning
• wireless transmission scheme