Adaptive Graph-Based Federated Multi-Agent DRL for 6G UAV Swarm Efficiency

In disaster recovery scenarios where communication infrastructure is compromised, unmanned aerial vehicles (UAVs) are essential for real-time communication and computational services. However, optimizing UAV swarm performance for ground terminals involves addressing challenges such as task management, energy efficiency, UAV coverage, load balancing, and real-time coordination in dynamic environments. To address these challenges, we propose a variational autoencoder (VAE)-enhanced asynchronous federated learning (FL) framework integrated with graph neural network (GNN)-based multi-agent deep reinforcement learning (DRL) for 6G-enabled UAV swarms. The framework utilizes ultra-reliable low-latency communication (URLLC)-enabled semantic communication to optimize a utility function that balances task completion, energy consumption, coverage, and load balancing. The VAE detects anomalies and enhances task prioritization and resource allocation, while the GNN-based multi-agent DRL framework ensures effective swarm coordination and enables decentralized, autonomous decision-making throughout the UAV network. Extensive simulations demonstrate the effectiveness of our approach in enhancing UAV swarm efficiency in disaster recovery operations.