# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 1 Introduction  
In recent years, with the popularity of smart devices and the continuous expansion of the Internet of Things\({}^{\prime}\) scale, a quantity of the data show an exponentially increasing trend [1]. Through the analysis and processing of these data, some intelligent applications (face recognition, intelligent driving, voice recognition) have been developed rapidly, but at the same time, it also raises the issue of data security and privacy. In addition, as people gradually pay more attention to privacy issues, more and more entities begin to emphasize the attribution and use rights of data, reducing the data exchange between different entities, making each entity gradually become an \"isolated data island\" [2]. So as to solve the above problems, give greater play to the value of data and improve the performance of artificial intelligence algorithms, scholars have put forward a new paradigm, Federated Learning (FL), which has the ability to crack the \"isolated data island\" problem and protect privacy. Federated learning was first proposed by [PERSON] et al. [3], as it can coordinate the joint learning of all parties without exposing the data of all parties. Compared with traditional centralized learning, it not only improves the utilization rate of wireless resources, but also guarantees the privacy of users. In the process of FL, participants do not need to upload local original data, they only need to upload data-related neural network model parameters, and then the master server security aggregates model parameters, and provides feedback to participants. Then, participants according to their own dataset update the global model, effectively ensuring the security and privacy of eachparticipant's sensitive data. Although FL can effectively solve the problems of the \"isolated data island\" and privacy, it also faces great challenges: the participant problem and the communication problem. In the FL process, as more participants have increased data diversity, the learning ability of the trained model will be stronger. Moreover, the model parameters need to be exchanged frequently among all parties, and the central party often needs to wait for all user's training models to be uploaded before it can conduct security aggregation or other data processing. For example, in case of user drop off or communication block, etc. The central party will be trapped in the infinite waiting process, which seriously affects the efficiency of model aggregation and FL. Therefore, the current research focuses of FL are how to establish a perfect incentive and selection mechanism, encourage more participants to join, select participants with high quality data, how to propose a way to ensure the FL efficiency and how to improve the quality and capacity of communication channels. On the one hand, the participant problems of FL over wireless networks have been studied in [4; 5; 6; 7; 8; 9]. In [4], [PERSON] et al. investigated user scheduling algorithms that maximize the number of participating users in each global round to improve FL test performance. Motivated by [4], [PERSON] et al. [5] proposed a client scheduling scheme based on the multi-armed bandit (MAB) theory, which balanced the selection of clients with uncertain resources and known large resources. In addition, [PERSON] et al. [6] proposed an online client scheduling scheme based on the greedy algorithm, which reduces the number of training rounds and the time interval of each global round. Based on age of update (AOU) measurements, [PERSON] et al. [7] proposed a scheduling strategy, which jointly considers the stability of received parameters and instantaneous channel quality to improve FL operation efficiency. In addition, [PERSON] et al. [8] investigated the gradient average effect over participating clients in each global round, and proposed a scheduling strategy to select high-quality clients by taking into account the diversity of channels and the [PERSON]importance\" of clients' updates.