Cooperative Communication Task Offloading With Untrustworthy ARIS Swarm via Federated Security Reinforcement Learning

Abstract

Uncrewed aerial vehicle (UAV) swarm provides effectively and diversely applications in low-altitude networks. However, due to the non-stationary open nature wireless environments, and the untrusted UAVs can participate in wireless communication tasks, which cause severe security threats. In this paper, we consider an integrated communication, sensing, computation and security framework, where the swarm UAV carried reconfigurable intelligent surfaces (ARIS) cooperative for communication task offloading with the existence of untrustworthy ARISes. Specifically, we propose a secure collaborative communication task offloading framework, which is federated multi-agent deep deterministic policy gradient with the score-trust based aggregation refinement (FedMADDPG-STAR) strategy. In this framework, each ARIS agent learns local trajectories under limited observations and periodically uploads model parameters rather than the raw data to a global server for aggregation via FedMADDPG. On the other hand, the proposed STAR algorithm enhances robustness by comprehensively considering the consistency of training directions, training progress, and multivariate anomaly indicators, thereby enabling adaptive trust scoring and isolation of untrustworthy ARISes. In addition, to increase the training stability and the efficiency of the proposed FedMADDPG-STAR, we design the refined reward function and employ the fast optimal phase-shift configuration. The system complexity is then analyzed. Simulations comparing different robust aggregation methods under various Byzantine attacks demonstrate that our proposed STAR scheme outperforms the typical aggregation approaches. We find that the proposed FedMADDPG-STAR framework can achieve secure and efficient communication task offloading under the non-stationary open nature wireless environments and the Byzantine attacks.