Deep Federated Learning Enhanced Secure POI Microservices for Cyber-Physical Systems

An essential consideration in cyber-physical systems (CPS) is the ability to support secure communication services, such as points of interest (POI) microservices. Existing approaches to support secure POI microservices generally rely on anonymity and/or differential privacy technologies. There are, however, a number of known limitations with such approaches. Hence, this work presents a deep-federated-learning-based framework for securing POI microservices in CPS. In order to enhance data security, the system architecture is designed to isolate the cloud center from accessing user data on edge nodes, and an interactive training mechanism is introduced between the cloud center and edge nodes. Specifically, edge nodes pre-train reliable deep-learning-based models for users, and the cloud server coordinates parameter updating via federated learning. The connected and isolated structure between cloud center and edges facilitates deep federated learning. Finally, we implement and evaluate the performance of our proposed approach using two real-world POI-related datasets. The results show that our proposed approach achieves optimal scheduling performance and demonstrates its practical utility.