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Related Experiment Video

Updated: Sep 15, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Diffusion-Driven Proxy Learning Strategy with Secure Peer Interactions for Generative Intelligence in Cyber-Physical

K M Karthick Raghunath1, T R Mahesh2, Surbhi Bhatia Khan3

  • 1Department of Computer Science and Engineering, JAIN (Deemed-to-be University).

Journal of Visualized Experiments : Jove
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Summary

The Generative Proxy Learning Framework (GPLF) enhances Generative AI in Cyber-Physical Systems (CPS) by enabling secure data analysis and synthetic data generation. This approach improves anomaly detection and predictive modeling while protecting sensitive information.

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Area of Science:

  • Cyber-Physical Systems (CPS)
  • Generative Artificial Intelligence (AI)
  • Machine Learning
  • Data Security and Privacy

Background:

  • Cyber-Physical Systems (CPS) integrate computational intelligence with physical processes for monitoring and automation.
  • Deploying Generative AI in CPS is challenging due to distributed environments, sensitive data, and privacy concerns.
  • Existing Federated Learning (FL) methods struggle with model diversity and privacy risks in CPS.

Purpose of the Study:

  • To introduce the Generative Proxy Learning Framework (GPLF) for secure Generative AI applications in CPS.
  • To address privacy and security challenges in distributed CPS environments using advanced AI techniques.
  • To enhance generative AI capabilities for anomaly detection and predictive modeling within CPS.

Main Methods:

  • The Generative Proxy Learning Framework (GPLF) utilizes Proxy-based Federated Learning (ProxyFL) adapted for Generative AI in CPS.
  • Each participant maintains a private model for local data and a shared proxy model for secure collaboration.
  • Advanced Diffusion Models generate high-fidelity synthetic sensor data, preserving key features, with differential privacy and encryption for secure updates and communication.

Main Results:

  • GPLF demonstrated a 25% reduction in privacy leakage and a 25% improvement in data exchange capabilities.
  • Generative task accuracy improved by 18% in benchmark CPS datasets.
  • The framework enables secure generative processes, including anomaly detection, synthetic data creation, and predictive modeling.

Conclusions:

  • The Generative Proxy Learning Framework (GPLF) offers a transformative solution for secure and intelligent operations in Cyber-Physical Systems.
  • GPLF effectively balances the need for data analysis and model training with robust privacy and security guarantees.
  • The framework's ability to generate realistic synthetic data enhances the utility of Generative AI in critical CPS applications.