Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately.

翻译：联邦边缘学习（FEEL）通过边缘设备与服务器之间的周期性通信实现隐私保护的模型训练。搭载无人机的边缘设备因其灵活性和机动性在高效数据收集中具有显著优势，特别适用于FEEL场景。在无人机辅助的FEEL中，感知、计算与通信相互耦合且竞争有限的机载资源，而无人机部署也会影响感知和通信性能。因此，无人机部署与资源分配的联合设计对于实现最优训练性能至关重要。本文以基于无线传感的人体动作识别为具体案例，研究了FEEL中无人机部署设计与资源分配的联合优化问题。我们首先分析了无人机部署对感知质量的影响，并确定了能够保证数据样本质量满足要求的感知仰角阈值。针对非理想感知信道，我们采用概率感知模型，其中每架无人机的成功感知概率由其位置决定。随后，我们推导出FEEL训练损失的上界作为感知概率的函数。理论结果表明，当无人机具有均匀的成功感知概率时，收敛速度可得到提升。基于此分析，我们在期望的最优性差距约束下，通过联合优化无人机部署、集成感知、计算与通信（ISCC）资源，建立了训练时间最小化问题。为求解这一具有挑战性的混合整数非凸问题，我们应用交替优化技术，提出了带宽、批大小与位置优化（BBPO）方案，对这三个决策变量进行交替优化。