## 4 Federated-Learning-Empowered UAV Computing
As a result, it is critical to think about how to construct an incentive mechanism for FL in UAV networks. Due to the lack of terrestrial connectivity and the battery limitations of FL users, conducting FL chores may be impossible. Therefore, the authors of [126] deployed UAVs and WPC for FL networks to overcome these difficulties. A UAV equipped with edge computing and WPC capabilities is deployed as an aerial energy source and an aerial server to conduct FL operations to allow sustainable FL solutions. Furthermore, the authors proposed an energy-efficient, combined approach for UAV placement, power control, transmission time, model accuracy, bandwidth allocation, and computing resources that aims to reduce the total energy consumption of the aerial server and users. **Channels propagation:**: The authors of [127] presented an FL-assisted categorization strategy in which each UAV performs local training on locally obtained pictures to generate a local model. Subsequently, each UAV sends its locally acquired model over a fading wireless channel, which generates a global model, then sends it back to each UAV for the next round of local training. In addition, a Weighted Zero Forcing (WZF) transmit precoding (TPC) based on genuine, imperfect channel state information is employed at each UAV to further reduce the computational cost. The authors of [128] developed UAV-assisted disaster relief networks based on blockchain and ML to accomplish safe and efficient data transfer. The authors initially described a lightweight blockchain-enabled collaborative aerial-ground networking architecture to ensure data transmission in a disaster, followed by a credit-based delegated proof-of-stake consensus protocol to improve consensus efficiency while encouraging UAVs to be honest. A new RL-based approach is designed to intelligently offload UAV computation missions to moving vehicles in the dynamic environment by using the idle processing resources of ground vehicles. **Reconfigurable Intelligent Surfaces (RIS):**: Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reflecting Surfaces (IRS), are programmable structures that can be used to engineer the wireless propagation environment to enhance network performance. In the context of UAV air-to-ground networking, the integration of RIS is being proposed to improve the communication security and performance [129; 130]. [PERSON] et al. [131] studied the UAV swarm-enabled ARIS (SARIS), including its motivations and competitive advantages over terrestrial RIS (TRIS) and ARIS, as well as its innovative wireless network applications. The authors focused on the beamforming design, SARIS channel estimate, and SARIS deployment and movement to solve the essential issues of developing the SARIS. With early numerical findings, the possible performance augmentation of SARIS was examined. To improve the performance of UAV-assisted air-ground networks, [PERSON] et al. [129] proposed using RIS. The authors provided an overview of UAVs and RIS by describing the many uses of RIS and the compelling characteristics of UAVs and the advantages of combining them [129]. The authors next looked at two case studies in which the UAV trajectory, transmit beamforming, and RIS passive beamforming are all optimized together. The average attainable rate of the relaying network is maximized in the first case study by mounting the RIS on a UAV. The RIS is used in the second case study to aid UAV-ground communication while battling an adversary eavesdropper. **Privacy:**: UAVs-based service providers for data gathering and AI model training, also known as UAVs-as-a-Service (or Drones-as-a-service, DaaS), have been increasingly popular in recent years. However, the strict restrictions controlling data privacy may make data exchange between independently owned UAVs difficult.