Byzantine-Resilient UAV Swarm Federated Reinforcement Learning for Low-Altitude Economy

Unmanned Aerial Vehicles (UAVs) are renowned for their agility. As UAV swarms take on increasingly complex collaborative tasks, they face a growing threat from Byzantine attacks, where compromised UAVs transmit misleading information, affecting missions and leading to failures. Unlike traditional attacks, Byzantine attacks on UAV swarms occur during the online learning of coordination, where real-time model updates are critical. Existing research largely overlooks this dynamic and evolving vulnerability. This paper proposes a novel trustworthy hierarchical aggregation algorithm within a federated reinforcement learning framework, specifically designed to defend against Byzantine attacks by ensuring robust and accurate aggregation of model parameters during the online learning process. This resilient aggregation mechanism significantly enhances the swarm's learning integrity in adversarial environments. We design a realistic UAV round-up scenario. Simulations demonstrate that UAV swarms with our algorithm maintain strong resilience, even under severe Byzantine attacks, highlighting its promise for secure and reliable UAV swarm intelligence.