## 1 Introduction
[29] introduced FL for solving a combined resource allocation and power issue, proving that vehicular users' power consumption may be beneficially lowered. The authors of [32] provided energy-efficient strategies for IoT devices regarding channel access schemes for computing offloading and resource management. However, UAV-assisted relaying was not taken into account. The authors of [33] studied ML and DL-based strategies in UAV-enabled MEC networks. However, the authors of [24] introduced Federated DL (FDL) for UAVs in wireless communication networks. Furthermore, the authors of [34] conducted a detailed assessment of computation offloading in UAV-enabled MEC, which was classified into partial, binary, and load relaying. Moreover, the authors of [35] discussed the data processing at the edge based on ML, which was a benefit and drawback in the context of UAVs. The authors of [36] introduced how edge AI will affect critical UAV technical applications and use. In comparison, the authors of [37] addressed where ML methods are applied to improve the network performance of UAVs. The authors of [38] discussed AI techniques related to enhancing Ground, Air, and Space (SAGIN) for remote areas. In Table 1, we provide a summary of related surveys. Installing terrestrial systems (i.e., BSs and APs) and utilizing MEC to provide computing tasks to everything in temporary events [39], disaster relief operations, and military operations [40] will not be cost-effective. However, a UAV can collect data and perform computing activities for everything that does not have direct access to BSs [41] due to its flexibility and computation capability. Furthermore, UAV computing can act as a relay station for everything beyond the BS's coverage area. Although UAV computing is a promising technology, it has several obstacles that must be overcome before being widely  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref. ** & **Survey Title** & **Highlight** & **A** & **B** & **C** & **D** \\ \hline [33] & UAV-enabled mobile edge computing & Using DL and ML approaches in UAV-enabled IoT based on AI: A comprehensive review & Using DL and MEC network architecture applications & \(\times\) & ✓ & ✓ & 4.0 \\ \hline [32] & Energy efficient UAV-enabled mobile edge computing for IoT devices: a review & Using UAV-enabled MEC for energy-efficient resource  
adopted. Firstly, a practical task offloading decision is necessary to decrease the energy consumption of smart things, UAVs, as well as latency. Secondly, because communication resources (i.e., bandwidth) are limited, an effective bandwidth allocation method at the UAV is required. Thirdly, efficient computing resource allocation techniques are required because the computing resources available in UAV computing are restricted. As a result, computing energy consumption at the UAV computing may grow due to the lack of appropriate allocation techniques, and offloaded device tasks may not be completed on time. We discuss various research related to UAV computing and their application and challenges. Therefore, the contribution of this paper is mainly focused on providing a comprehensive overview of the appropriate techniques that can be used to improve UAV computing performance in B5G networks for supporting services in smart and harsh environments. The contributions of this paper are summarized as follows:  
1. We introduce UAV computing and review the existing work about UAV computing. 2. We discuss how FL plays a vital role in improving UAV computing for enabling B5G. 3. We identify and discuss the application of UAV computing. The rest of the paper is organized as follows. Section 2 discusses the FL Paradigm which can facilitate the creation of a global learning model by multiple participants without sharing local data with each other. In Section 3, we discuss how UAV's computing power can be used as a mobile edge computing infrastructure to support the data processing needs of resource-constrained IoT devices.