Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks

Reconfigurable Intelligent Surface (RIS) is envisioned to revolutionize 6G wireless networks, particularly in complex environments like smart airports, by customizing analog beamforming with desired direction and magnitude. Through precise configuration refinement, the intelligent surface intends to achieve equivalent Quality of Service (QoS) with fewer antennas, thereby enhancing coverage and capacity in high-demand areas of airports. However, existing model-free algorithms struggle to obtain a stable policy gradient of intelligent surface configuration. Moreover, centralized channel estimation is inefficient to massive communication and more vulnerable to eavesdroppers. To address these challenges, a robust Proximal Policy Optimization-Huber (PPO-Huber) algorithm was developed to improve the efficiency and robustness of digital connectivity within airports. Concerning the privacy of channel models in massive communication, we proposed an optimal Differential Private Federated Learning (DPFL) with noise reduction, ensuring secure access to channel information. Comprehensive convergence analyses are conducted for each proposed algorithm to facilitate hyperparameter tuning and suggest potential research directions. Experimental results demonstrate that our algorithms not only offer flexible deployment of intelligent surface without accurate channel knowledge, but also substantially breaking the communication-privacy-utility trilemma in massive RIS-aided 6G wireless networks of smart airports.