Privacy-enhanced momentum federated learning via differential privacy and chaotic system in industrial Cyber–Physical systems

Abstract

By leveraging Industrial Cyber–Physical Systems (ICPSs), deep learning-based methods are applied to address various industrial issues. Due to privacy policy reasons, conventional centralized learning (CL) may be improper for some industrial scenarios with sensitive data, such as smart medicine. Recently, federated learning (FL) as a novel collaboration learning approach has received extensive attention, which can break data barriers between different institutions to improve the model performance. However, the privacy information of the industrial agents may be inferred from their shared parameters. In this paper, we propose a Privacy-Enhanced Momentum Federated Learning framework, named PEMFL, that amalgamates differential privacy (DP), Momentum FL (MFL) and chaos-based encryption method. During the training, differentially privacy is used to disturb the industrial agents’ gradient parameters in order to preserve their privacy information. Meanwhile, each industrial agent uses the chaos system-based encryption method to encrypt the weight parameters of their local models, which has two advantages: (1) the encryption method can enhance privacy protection; (2) the cloud server cannot access the truth value of the global model parameters which is a vital asset to the industrial agents. In addition, Momentum Gradient Descent (MGD) and an adjusting learning rate schedule are adopted to improve training efficiency for the PEMFL. The performance of the PEMFL is evaluated based on two non-i.i.d datasets. Theoretical analysis and experimental results demonstrate the excellent performance of the PEMFL in terms of accuracy and privacy security.