6G mmWave Security: Next-Gen Protection with Federated Learning

The rapid evolution of 6G networks has brought novel security and privacy challenges in managing user data, particularly with the extensive use of millimeter wave communications (mmWave) technology. Ensuring robust security and privacy preservation in this dynamic network environment is paramount. This paper explores a multifaceted approach to address these challenges, focusing on Federated Learning (FL) for mmWave beam prediction and utilizing outlier detection in the aggregation process to mitigate adversarial attacks like the Fast Gradient Sign Method (FGSM). The potential of FL to enhance beam prediction accuracy while safeguarding user data privacy is investigated by training models on edge devices and aggregating model updates rather than raw data. Furthermore, integrating outlier detection in the aggregation process identifies and filters malicious model up-dates that may compromise the FL system's integrity. Simulations assessing the robustness of the proposed system against various threats reveal substantial enhancement in data security, with an average 15.30% increase in defense effectiveness.