FLGuard: A Rényi-Differential Privacy Empowered Federated Learning Framework for 6G Networks

The transition from 5G to 6G is set to transform telecommunications, offering higher data rates, ultra-low latency, and the ability to connect billions of devices simultaneously. Artificial intelligence (AI) is crucial for this evolution, particularly through federated learning (FL), which enables secure model training while preserving data privacy. However, the decentralized nature of FL introduces security challenges, such as model poisoning and inference attacks, especially in sensitive 6G scenarios like smart healthcare and Vehicle to Everything (V2X) systems. To address these issues, we introduce FLGuard, a novel framework that combines FL as the aggregation component with Variational Auto-Encoder (VAE), Generative Adversarial Network (GAN), and Diffusion Model as generative modules, incorporating Rényi Differential Privacy (RDP) as its security mechanism. This architecture aims to improve the accuracy, responsiveness, and security of FL models. Additionally, we provide a case study that highlights FLGuard’s capability to withstand poisoning attacks, demonstrating its effectiveness in safeguarding privacy while enhancing the performance of FL models. Simulation results show that FLGuard consistently achieves high robustness and accuracy under various attack scenarios.