FedMES: Robust FL for UAV Swarm Tracking via Adaptive Model Enhancement

With the rapid development of intelligent sensing technology, unmanned aerial vehicles (UAVs) have shown their potential in mobile non-cooperative target (MNCT) tracking tasks. Collaborative learning of target motion characteristics is an effective means of improving tracking stability for UAV swarms. However, UAV swarm-based MNCT tracking faces critical challenges in real-world environments, including sensing interference and data heterogeneity. To address this and realize per-sensing-round updates of the models deployed on the UAV swarm, we propose FedMES, a federated learning framework that jointly optimizes data reliability and model performance. First, we design a data filtering mechanism using polynomial fitting and moving average correction to suppress destructive noise. Second, dynamic aggregation weights are assigned based on historical data confidence to prioritize high-reliability clients. Third, clients selectively adopt the global or local model via a dual-metric evaluation post-aggregation. Evaluated on real-world MNCT trajectories, FedMES reduces mean estimation errors by 10%-22% compared to FedAvg/FedProx, demonstrating superior robustness under high noise and data loss.