Multi-UAV-Assisted Wireless Federated Learning in Dynamic Environments: An AIRDO-Based Multi-Agent RL Approach

In dynamic wireless environments, fluctuations in user distribution and channel conditions challenge the stability and efficiency of federated learning (FL) systems, especially in multi-UAV-assisted scenarios. UAV-assisted wireless FL has drawn great attention for its flexible deployment and distributed computing capabilities. However, centralized aggregation faces the risk of single-node failure at aggregation nodes, which can severely impact system reliability in dynamic environments. To address these issues, we propose a Decentralized Aggregation in UAV-assisted wireless Federated Learning (DAUFL) framework, which replaces centralized aggregation with a multi-node model aggregation mechanism to maintain stable operation and enhance system reliability under node failures. Furthermore, we develop an Adaptive Interference-aware Resource allocation and Deployment Optimization (AIRDO) algorithm. It first employs Constrained K-means for efficient UAV coverage, then applies Multi-Agent Reinforcement Learning (RL) for dynamic interference-aware resource allocation and UAV deployment optimization. Simulation results show that DAUFL achieves strong robustness and model performance under both Independent and Identically Distributed (IID) and Non-IID data distributions, maintaining over 90% and 80% accuracy even under a 50% node failure rate. The AIRDO algorithm further accelerates convergence, reduces training latency, and maintains stable performance with large-scale users.