Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in
order to execute various tasks ranging from coordinated trajectory planning to
cooperative target recognition. However, due to the lack of continuous
connections between the UAV swarm and ground base stations (BSs), using
centralized ML will be challenging, particularly when dealing with a large
volume of data. In this paper, a novel framework is proposed to implement
distributed federated learning (FL) algorithms within a UAV swarm that consists
of a leading UAV and several following UAVs. Each following UAV trains a local
FL model based on its collected data and then sends this trained local model to
the leading UAV who will aggregate the received models, generate a global FL
model, and transmit it to followers over the intra-swarm network. To identify
how wireless factors, like fading, transmission delay, and UAV antenna angle
deviations resulting from wind and mechanical vibrations, impact the
performance of FL, a rigorous convergence analysis for FL is performed. Then, a
joint power allocation and scheduling design is proposed to optimize the
convergence rate of FL while taking into account the energy consumption during
convergence and the delay requirement imposed by the swarm's control system.
Simulation results validate the effectiveness of the FL convergence analysis
and show that the joint design strategy can reduce the number of communication
rounds needed for convergence by as much as 35% compared with the baseline
design.

本文提出了一种利用分布式联邦学习算法实现无人机群体中集中式机器学习的新框架并对其进行了收敛性分析，同时，提出了一种联合功率分配和调度设计来优化联邦学习的收敛速率，同时考虑了能量消耗和延迟要求。仿真结果验证了 FL 收敛分析的有效性，并表明联合设计策略可以将收敛所需通信轮数比基准设计减少 35％。