Diffusion-Driven Proxy Learning Strategy with Secure Peer Interactions for Generative Intelligence in Cyber-Physical System

Cyber-Physical System (CPS) blends computational intelligence with physical processes, which enables instant monitoring, decision-making capability, and automation services throughout various vital domains. Moreover, Generative Artificial Intelligence (AI) faces considerable barriers to deployment within CPS because distributed environments with sensitive data present serious privacy and security maintenance challenges. Current techniques, such as Federated Learning (FL), encounter difficulties both in their model diversity and the risk that privacy may be compromised. The Generative Proxy Learning Framework (GPLF) serves as our innovative solution that utilizes Proxy-based Federated Learning (ProxyFL) specifically adapted for Generative AI applications within Cyber-Physical Systems (CPS). In GPLF, each participant maintains two models: Participants operate a private model dedicated to local data analysis together with a shared proxy model that enables protected node collaboration. As the essential foundation of generative AI mechanisms, advanced Diffusion Models deliver high-fidelity synthetic data together with key data feature preservation. The models generate synthetic sensor data, which enables improved anomaly detection and supports predictive modeling through authentic CPS behavior representations under various scenarios. The system achieves advanced privacy protection with differential privacy mechanisms in proxy data updates, while direct peer communication in the network benefits from advanced encryption protections. GPLF serves CPS platforms by connecting to real-time sensors and IoT devices that support secure generative processes, including anomaly detection, synthetic data creation, and predictive modeling. Test results from benchmark CPS datasets show considerable performance improvements with 25% less privacy leakage and 25% better data exchange capabilities, together with an 18% improvement in generative task accuracy to support its transformative potential for secure, intelligent CPS operations.