# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 1 Introduction
An FL task-training model based on contract theory was proposed in [9], which minimized the incentive budget according to the individual rationality (IR) and incentive compatibility (IC) of users in each FL training round. However, the authors of [4; 5; 6; 7; 8; 9] only started from the perspective of how to select and motivate users, and did not consider the optimization of the clients' energy consumption. When too many clients participate, as the number of wireless resources is fixed, the clients' energy consumption will [PERSON] sharply, which is unfavorable to clients with limited energy storage. Moreover, if users suddenly quit the learning process, it will also have an impact on learning performance. On the other hand, the communication problems of FL over wireless networks have been investigated in [10; 11; 12; 13]. In [10], [PERSON] et al. researched the computation and communication resource allocation of FL on wireless networks, and under the condition that constraints are met, an iterative algorithm was proposed to derive the closed-form solutions of transmission time, transmission bandwidth, transmission power, computation frequency and local accuracy. [PERSON] et al. [11] introduced a Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation was partially migrated from the cloud to the edge server, and formulated a joint wireless resource allocation and user edge association problem to minimize the energy and latency. [PERSON] et al. [12] proposed a new computation and communication resource framework, which uses multiple processors of clients to simultaneously process data, and jointly controls bandwidth allocation, CPU-GPU workload allocation, computation frequency of processors and transmission time. In addition, the study in [12] was further extended by [PERSON] et al. [13], within a certain time budget, solving the problem of resource allocation in the HFEL structure. However, in [10; 11; 12; 13] the authors were only concerned with the optimization of the client energy without setting a suitable client scheduling scheme. When the number of users reaches a critical value, the data owned by the users may contain all the data features of the overall dataset. Therefore, the FL learning performance will not be significantly improved with the increase in the user numbers. In addition, some users' data quality may be very poor, and if this user participates, the FL learning performance will be degraded. In summary, in [4; 5; 6; 7; 8; 9; 10; 11; 12; 13] the proposals have only considered the participant or communication problem in FL unilaterally, without comprehensively thinking about the effect of user choice and terminal energy consumption in FL. So, in the work of [14], [PERSON] et al. put forward an auction game as an incentive mechanism between the base station (BS) and users, with each user submitting bids based on the minimal energy consumption to participate in FL, and a primal-dual greedy auction mechanism was introduced to determine the winners and maximize social welfare. In the case that the uplink transmission may be wrong, [PERSON] et al. [15] formulated the optimization problem of minimizing FL loss function and proposed an optimization scheme of wireless resource allocation and user selection. Under the framework of HFEL, [PERSON] et al. [16] introduced a user selection and resource allocation strategy to take into account the uncertainty of wireless channels and the importance of the weighted gradient. Moreover, in the case of users' data that are independently identically distributed (IID) and non-IID, [PERSON] et al. [17] studied the optimization problem by optimizing user association and resource allocation. In [18], [PERSON] et al. proposed a jointly optimized user selection and resource allocation strategy to maximize the FL test accuracy within a given total FL completion time. In particular, based on a novel technique, namely over-the-air computation, [PERSON] et al. [19] investigated client scheduling and beam-forming design in FL. In addition, [PERSON].