Title: Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning Show Chinese title

Title: 用于无人机辅助联邦边缘学习的集成感知、计算和通信

Abstract: 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. Show Chinese abstract

Abstract: 联邦边缘学习（FEEL）通过边缘设备和服务器之间的周期性通信实现隐私保护的模型训练。搭载在无人机（UAV）上的边缘设备由于其灵活性和高效数据收集能力，在FEEL中具有显著优势。在无人机辅助的FEEL中，感知、计算和通信相互耦合并竞争有限的车载资源，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。本文通过基于无线感知的人类运动识别的具体案例研究，解决了FEEL中无人机部署设计与资源分配的问题。我们首先分析了无人机部署对感知质量的影响，并确定了一个保证数据样本质量满意的感知仰角阈值。由于非理想感知信道的存在，我们考虑了概率感知模型，其中每个无人机的成功感知概率由其位置决定。然后，我们将FEEL训练损失的上界表示为感知概率的函数。理论结果表明，如果无人机具有均匀的成功感知概率，则收敛速度可以提高。基于此分析，我们在期望的最优性间隙约束下，通过联合优化无人机部署、集成感知、计算和通信（ISCC）资源，提出了一个最小化训练时间的问题。为了解决这个具有挑战性的混合整数非凸问题，我们应用交替优化技术，并提出带宽、批量大小和位置优化（BBPO）方案，以交替优化这三个决策变量。