## 1 Introduction
Then, the UAV sends the computing results to the ground users, such as the identification results, environment analysis, stitching of video streams, and so on. The authors of [14] investigated the challenge of minimizing computational energy consumption between IoT devices and UAV by concurrently optimizing task offloading decision making, resource allocation during transmission, and the UAV's trajectory. The authors of [15] investigated the topic of minimizing overall mobile energy consumption in a UAV-based mobile cloud computing system. The cloudlet's resource allocation and trajectory were optimized with orthogonal and nonorthogonal multiple access. The authors of [16] introduced the combined design of computing offloading and resource allocation and the UAV trajectory for minimizing energy consumption and task completion time in a UAV computing for the IoT. The energy reduction in UAV-enabled edge computing by making smart offloading decisions, distributing transmitted resources in downlink and uplink, and designing UAV path was presented in [17]. The authors of [18] discussed how to reduce the overall required energy of a UAV by optimizing the transmit power, offloading amount,and UAV path in a UAV-enabled wireless powered collaborative computing scenario. A novel UAV computing was discussed in [19], with a reduced weighted total of all IoT device service delays and UAV energy consumption by optimizing UAV communication, position, and computing resource allocation and task splitting choices at the same time [20; 21; 22]. The authors of [23] presented how to reduce UAV computing energy consumption in the binary offloading mode. Machine Learning (ML) has demonstrated its power to provide intelligence to wireless networks, including IoT and UAV networks. However, traditional ML techniques are cloud-centric, requiring all data to be transmitted to a cloud data center and processed there, which may not be appropriate for UAV networks [24]. To begin with, the data created by each UAV may be unavailable owing to privacy risks since it may contain sensitive information (e.g., the identification and location of the UAV). Secondly, for real-time UAV applications, the delay encountered from providing raw data to obtaining a well-trained model may be unacceptable. Finally, sending large amounts of raw data such as images and videos to the cloud uses a lot of energy and bandwidth, which is inconvenient for UAV networks with limited power and bandwidth. As a result, it would be useful if ML model training could be performed directly inside the UAV networks without transferring data out to the cloud. Recently, Federated Learning (FL), a decentralized ML approach [25; 26], has been proposed to solve similar difficulties in which each participating user learns a model based on its local dataset. The local model modifications are then communicated to a parameter server so that a shared global model may be updated. Thus, the UAVs are fed the revised global model for the next round of local training. Local model changes are communicated back to the server in FL instead of the traditional case of sending back the training datasets to the cloud. As a result, training models may be shared while maintaining anonymity, and communication costs can be lowered. Because of these compelling benefits,  
Figure 1: Scenarios of MEC implemented using UAV computing. researchers are increasingly focused on using FL in wireless communications [27; 28; 29; 30; 31]. FL was used in resource-constrained MEC [30], while it was also used in IoT applications [28]. [PERSON] et al. [27] discussed a reputation-based worker selection approach for boosting FL's performance, using advanced blockchain-assisted techniques for secure reputation management. It was also used to solve intercell interference and scheduling issues in wireless networks [31]. Furthermore, the performance could be achieved when the signal to interference plus noise ratio was high enough. In the Ultra-Reliable Low-Latency Communication (URLLC) of vehicle networks, [PERSON] et al.