Ensuring the federation correctness: Formal verification of Federated Learning in industrial cyber-physical systems

Abstract

In industry 4.0, Industrial Cyber–Physical Systems (ICPS) integrate industrial machines with computer control and data analysis. Federated Learning (FL) improves this by enabling collaborative machine learning and improvement while maintaining data privacy. This method improves the security, and intelligence of industrial processes. FL-based frameworks proposed in the literature do not perform rigorous validation of collaborators’ behaviors, especially with regard to reliability and operational correctness. In contrast, non-FL-based cyber–physical systems have already been verified in the literature using formal methods. Therefore, there is a significant gap in the application of these verification techniques to FL-based systems. To fill this gap, we explore the possibility of introducing formal verification into FL-based cyber–physical systems, starting with our FedGA-Meta published framework. Thus, our research focuses on expanding our FedGA-Meta framework in the context of Industry 4.0, this paper delves into a comprehensive validation of the framework’s operational reliability and correctness within ICPS based on FL. To achieve this, we employ Timed Computation Tree Logic (TCTL) for the precise specification of system requirements, coupled with Labeled Transition Systems (LTS) to construct the ICPS semantic in detail. Through the usage of Uppaal for both simulation and model-checking purposes, we rigorously test the framework under a variety of operational scenarios. This approach allows us to confirm the system’s reliability and correctness, ensuring that the FedGA-Meta framework operates effectively and as intended within the demanding environments of Industry 4.0.