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

Summary

Here we present the Generative Proxy Learning Framework (GPLF) that brings Proxy-based Federated Learning (ProxyFL) to improve Generative AI solutions in Cyber-Physical Systems (CPS). By integrating differential privacy features and encryption methods, GPLF enhances privacy protection, which reduces privacy leakage, thereby making Cyber-Physical System operations smarter and safer.

Abstract

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.

Introduction

The research investigates Cyber-Physical Systems (CPS) by combining computational intelligence with real-world processes to enable real-time surveillance alongside rapid decision-making capabilities and system automation¹. Emerging Internet-of-Things (IoT) and Artificial Intelligence (AI) technologies are significantly expanding the range of applications where CPS systems operate essential functions across smart grid development and industrial automation processes, as well as healthcare delivery services². Organizations deploying CPS are increasingly utilizing Generative AI models, which provide the capability to imitate....

Protocol

The Generative Proxy Learning Framework (GPLF) represents a novel technology that integrates Generative AI with CPS and resolves important issues of data privacy alongside security and performance metrics within distributed network systems. The functionality of CPS platforms depends on up-to-date monitoring alongside automated operations that extract sensitive data inputs from a growing number of IoT devices and sensors. The adoption of Generative AI technologies into CPS systems has been found to introduce special hazards like privacy vulnerabilities combined with security challenges throughout distributed network setups. GPLF rolls out an innovative Proxy-Based Fede....

Results

Privacy Leakage Reduction Index (PLRI) metric measures privacy leakage reduction when compared with standard baseline models. The evaluation focuses on how differential privacy and homomorphic encryption perform as privacy preservation approaches.

The privacy leakage score assesses the number of exposed data points relative to total updates in models, along with synthetic data distribution activities. It evaluates the effectiveness of privacy-preserving strategies. Systems achieve superior pri.......

Discussion

The design elements of GPLF not only support its privacy functions but also deliver supplementary benefits enhancing its deployment capacity. By employing diffusion models to produce high-fidelity synthetic data, the framework provides essential privacy protection layers for essential fields like healthcare alongside critical infrastructure monitoring while maintaining precise generative modeling capabilities. GPLF achieves both enhanced privacy protection and higher collaborative learning efficiency within heterogeneous.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
A100 GPU (CUDA)	NVIDIA	CUDA Version 11.6	GPU acceleration for model training and evaluation.
AMD EPYC-7502P CPU	AMD	N/A	Processor used for high-performance computing.
Gigabit Ethernet	Intel	N/A	Networking for peer-to-peer secure communication in CPS.
Matplotlib	Python Software Foundation	Version 3.5	Visualization library for plotting results.
Paillier Cryptosystem	Open Source (implemented via TenSEAL)	N/A	Enables additive homomorphic encryption on gradients.
PySyft	OpenMined	Version 0.6.0	Differential privacy and federated learning library.
Python (Anaconda Distribution)	Anaconda Inc	Version 3.9	Includes pre-installed packages and environment management tools, Used for scripting and framework development.
PyTorch	Meta AI	Version 1.12	Deep learning framework for training models.
RAM	Corsair	256 GigaByte (GB)	High memory support for intensive training.
Scikit-learn	Python Software Foundation	Version 1.1	Machine learning tools for performance evaluation.
Seaborn	Python Software Foundation	Version 0.11	Statistical data visualization library.
SSD Storage	Seagate	1 TeraByte (TB)	For fast data storage and retrieval.
TenSEAL	OpenMined	Version 0.3	Homomorphic encryption library for secure aggregation.
TensorFlow	Google	Version 2.9	Deep learning framework for diffusion models.
Ubuntu OS	Canonical	Version 20.04 LTS	Operating system used for all experiments.