Federated learning (FL) involves several devices that collaboratively train a
shared model without transferring their local data. FL reduces the
communication overhead, making it a promising learning method in UAV-enhanced
wireless networks with scarce energy resources. Despite the potential,
implementing FL in UAV-enhanced networks is challenging, as conventional UAV
placement methods that maximize coverage increase the FL delay significantly.
Moreover, the uncertainty and lack of a priori information about crucial
variables, such as channel quality, exacerbate the problem. In this paper, we
first analyze the statistical characteristics of a UAV-enhanced wireless sensor
network (WSN) with energy harvesting. We then develop a model and solution
based on the multi-objective multi-armed bandit theory to maximize the network
coverage while minimizing the FL delay. Besides, we propose another solution
that is particularly useful with large action sets and strict energy
constraints at the UAVs. Our proposal uses a scalarized best-arm identification
algorithm to find the optimal arms that maximize the ratio of the expected
reward to the expected energy cost by sequentially eliminating one or more arms
in each round. Then, we derive the upper bound on the error probability of our
multi-objective and cost-aware algorithm. Numerical results show the
effectiveness of our approach.

通过统计无人机增强无线传感器网络的统计特性，并利用多目标多臂赌博机理论开发了一种模型和解决方案，以在最大化网络覆盖的同时最小化联邦学习延迟；此外，还提出了另一种解决方案，特别适用于具有严格能量约束的大动作集，在每轮中逐步消除一个或多个动作，使用标量化的最佳臂识别算法找到最佳臂，使期望回报与期望能量消耗之比最大化。同时，导出了多目标和成本感知算法的误差概率的上界，在数值结果中展示了我们的方法的有效性。