Paper Title
Privacy-Preserving Generative Adversarial Networks for Synthetic Data Generation in Resource-Constrained Environments

Abstract
Privacy-preserving Generative Adversarial Networks (GANs) have shown promise in generating synthetic data while protecting individual privacy. However, deploying such models in resource-constrained environments, such as edge devices or low-power systems, poses significant challenges. This paper proposes novel techniques for optimizing privacypreserving GANs to operate efficiently in resource-constrained environments. Specifically, we explore techniques for model compression, quantization, and decentralized training to enable the deployment of privacy preserving GANs on devices with limited computational resources. We evaluate the performance and privacy guarantees of our proposed methods through extensive experimentation on real-world datasets. Our research contributes to advancing the field of privacy-preserving machine learning by enabling the widespread adoption of GANs in resource-constrained environments without compromising privacy or performance. Keywords - Generative Adversarial Networks, Edge Devices