Secure and Robust AI-Driven Beamforming for Terahertz (THz) 6G Networks: A Federated Learning Approach

The rapid evolution of wireless communication has driven the need for sixth-generation (6G) networks, which aim to deliver unprecedented data rates, ultra-low latency, and seamless connectivity. Terahertz (THz) frequencies are a cornerstone of 6G technology due to their vast spectrum availability, but they introduce new challenges such as severe path loss, atmospheric attenuation, and security vulnerabilities. To overcome these issues, AI-driven beamforming has gained attention as a powerful solution for optimizing signal transmission and interference mitigation. However, existing AI-based methods remain susceptible to adversarial attacks, privacy breaches, and suboptimal adaptation in dynamic environments [1]. This paper introduces a federated learning (FL)-based AI-driven beamforming approach tailored for THz-enabled 6G networks. The framework ensures privacy-preserving intelligence by training beamforming models collaboratively across distributed edge devices, eliminating the need for centralized data sharing. To enhance security, we integrate adversarial defense techniques, strengthening resilience against potential attacks that could degrade beamforming accuracy. Through extensive simulations, we evaluate key performance metrics, including beamforming efficiency, spectral efficiency, signal-to-noise ratio (SNR), and resistance to adversarial perturbations. Our results indicate that the proposed FL-based beamforming approach improves adaptability, mitigates security threats, and enhances overall network performance compared to traditional centralized AI models. This study provides a scalable and secure AI-driven solution for 6G beamforming, paving the way for reliable and privacy-aware THz communications. Future work will explore real-world deployment and the integration of quantum-secure encryption techniques to further fortify security in 6G networks.