ACLI-DPFL: Differentially Private Federated Learning with Adaptive Clipping and Local Iteration

Federated Learning (FL) is a distributed machine learning method, in which multiple devices or organizations perform training by sharing only models without sending their training data to a central server. To protect privacy, FL methods that apply differential privacy by adding noise have attracted significant attention. However, mitigating the accuracy degradation caused by the noise remains a critical challenge. This paper proposes a novel privacy-preserving FL method that integrates two approaches of adjusting the communication timing between the clients and the central server and regulating the noise. Experiments demonstrated that the proposed method achieves higher model accuracy.