Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost.

翻译：无人机在现代智慧城市中扮演着货物配送、实时道路车流测绘及污染监测等多种角色。无人机执行这些功能通常需要机器学习技术的支持。然而，面向无人机的传统机器学习模型面临数据隐私问题、通信成本与能量限制。联邦学习作为一种新兴的分布式机器学习方法，是解决上述问题的优秀方案。联邦学习允许无人机在不传输原始数据的情况下训练本地模型。但现有联邦学习需要一个中央服务器来聚合无人机的训练模型参数。中央服务器的故障会严重影响整体训练进程。本文基于现有无人机网络去中心化联邦学习架构，通过引入独特的去中心化联邦聚合方法，提出了两种聚合方式：交换型联邦学习和交替型联邦学习。这两种方法可通过控制本地训练轮次、本地通信与全局通信次数，有效管控能耗与通信成本。同时展示了所提训练方法的仿真结果，以验证该架构相比两种基准方法（如标准机器学习训练与标准单聚合服务器训练）的可行性与效率。仿真结果表明，所提方法在运行稳定性、能耗及通信成本方面均优于基准方法。