FedWT: Federated Learning with Minimum Spanning Tree-based Weighted Tree Aggregation for UAV networks

Abstract

In recent years, advances in communication technology, hardware, and deep learning have led to significant advancements in Unmanned Aerial Vehicles (UAVs). However, applying federated learning in UAV environments is challenging due to network instability and dependency on central server. In this paper, the Federated Learning with Minimum Spanning Tree (MST)-based Weighted Tree Aggregation (FedWT) is proposed to address transmission failure, delayed model updates, and single point of failure problems. FedWT uses MST to minimize model exchange during local aggregation, and addresses data heterogeneity through dynamic weighted averaging. It includes a decentralized federated learning method for UAVs, model sharing path scheduling to reduce communication overhead, and a flexible weight-based aggregation approach to handle data heterogeneity. Simulation results demonstrate the superior performance and communication efficiency of FedWT compared to traditional federated learning methods. In particular, FedWT achieves up to 2% higher prediction accuracy and reduces communication traffic by up to 84.98% in highly heterogeneous data scenarios. Under different network topologies, FedWT consistently outperformed other federated learning methods in terms of learning accuracy and loss reduction. Future work will optimize the dynamic weight adjustment and validate the robustness of FedWT under various scenarios.