# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 1 Introduction
[PERSON] et al. [20] studied the optimization problem of multi-layer FL under non-orthogonal multiple access (NOMA), and proposed a resource allocation scheme with the total energy consumption of Internet of Things' devices as the optimization objective. Moreover, [PERSON] et al. [21] studied FL in the scenario of the Internet of Vehicles, considering the position and speed of vehicles, and a minimum-maximum optimization problem was formulated to optimize the computation frequency, communication power and local accuracy to achieve the minimum cost. As an aerial mobile-base station, UAVs can quickly build a wireless network and provide emergency communication services. It plays an important role in earthquakes, floods, fire and other emergencies as well as battlefield operations. Due to their mobility, compared with fixed base stations, UAVs can find a suitable location to reduce the transmission energy consumption of users. Therefore, with the purpose of further improve the FL performance, UAVs can be combined with FL. [PERSON], [PERSON] et al. [22] proposed a novel and high-performing FL scheme to improve FL performance. By considering the unavailability of ground BS and the limited energy of users, [PERSON]. [PERSON] et al. [23] deployed a UAV equipped with edge computation and wireless power communications (WPC) capabilities to perform FL missions, and proposed an algorithm that jointly optimize the UAV's position, local accuracy and wireless resources to minimize the energy consumption of UAVs and users. Based on the premise that UAV-assisted users upload models, [PERSON]. [PERSON] et al. [24] established the coalition formation game to maximize the sum of UAV individual profit, according to users' preferences over heterogeneous UAVs, and proposed a joint auction-coalition to achieve a stable partition of the UAV coalitions and applied an auction scheme to solve the allocation of the UAV coalition. Under the limitation of learning accuracy and training delay, [PERSON] et al. [25] proposed a jointly optimized UAV placement and resource allocation to reduce the energy consumption of use. In addition, based on reinforcement learning, [PERSON] et al. [26] proposed an asynchronous federated learning (AFL) framework, which implemented asynchronous distributed computation, and proposed an asynchronous advantage actor-critic (A3C) reinforcement learning algorithm by joint optimizing device selection, UAVs' position and resource allocation to improve the FL convergence speed and test accuracy. However, in [23; 24; 25; 26], the altitude of the UAV is fixed. In fact, the UAV's coverage area should be related to the UAV's altitude. The higher the hover position of the UAV, the greater the chance of air-to-ground line of sight transmission, that is, the larger the coverage radius, which means the larger the service range of the UAV and more users can access, the faster the FL converges. However, the higher the deployment height, the larger the signal transmission distance will be, resulting in greater path loss and increased energy consumption of users participating in FL. Therefore, the height deployment of UAVs needs to be optimized. Based on the above analysis, in the UAV-assistedfederated learning wireless scenario, this paper takes into account the impact of the UAV's height on the coverage area, and under the constraint of completing the total delay, realizes the balanced optimization of users' total energy consumption and FL performance through the joint optimization of the UAV's position (height, horizontal position), local accuracy and computation and communication resources. The main contributions of this paper are summarized as follows.