GenAI-Based Privacy-Preserving Transfer Learning

Abstract

The rapid advancement of 5G technology has significantly accelerated the development of 6G standards, driving the need for even faster and more reliable connectivity. In this evolving landscape, the Integrated Sensing Digital Framework (ISDF) promises to revolutionize Industrial Cyber-Physical Systems (ICPS) by enabling unmatched real-time data acquisition, paving the way for groundbreaking advancements. ICPS nodes operate in diverse environments with varying levels of data availability. High-quality predictive models require large volumes of data, which may be abundant at some nodes and scarce at others. Instance-based transfer learning can address this disparity by transferring data from nodes with abundant data to those with limited data. However, this approach risks breaching data privacy, especially in sensitive ICPS applications such as industrial IoT (IIoT). To tackle this issue, we propose a novel privacy-preserving transfer learning approach based on generative models, specifically Generative Adversarial Networks (GANs). In this approach, a GAN is trained at a central center or node with access to large volumes of data. The generative model of the GAN is then transferred to nodes with data scarcity. Extensive experimental results on several real-world traffic load datasets confirm the effectiveness of this approach, highlighting its potential to enhance data-driven decision-making in ICPS while preserving data privacy.