## 4 Federated-Learning-Empowered UAV Computing  
Recently, FL has emerged as a viable distributed ML paradigm to address the shortcomings of traditional cloud-centric ML, which relies on a single entity to aggregate continuous ML models. FL protects device privacy, improves perceived latency, and relieves bandwidth and energy strain by allowing numerous devices to train an ML model cooperatively without sending the raw data out. As a result, FL is more suited for wireless edge networks than cloud-centric ML [62]. FL allows wireless edge devices to simultaneously learn a shared ML model while maintaining all raw data on the device. Moreover, numerous FL paradigms, such as collaborative FL [113], multihop FL [114], and fog learning [115], have been discussed to better adapt to the features of wireless edge networks, such as multihop,while a first FL framework for UAV networks was presented in [116]. Figure 5 depicts an FL-powered UAV computing collaboration scenario. Virtually all current FL paradigms are still centralized, which means that a single individual is responsible for ML model accumulation and fusion over the existing network, which can result in a single point of failure and is unsuitable with UAV networks with both unreliable links (or fluctuating links because of unpredictable UAV mobility) and nodes (due to node failures because of UAVs' drained-out on-board battery). They could run into a single point of failure problem, which is undesirable for UAV networks with unreliable nodes and connections because FL training would also have to end if the UAV ran out of energy or the wireless links among the UAVs failed due to UAV mobility, for example. It is worth mentioning that much research on decentralized ML exists, such as in [117], whereas the authors of [118] are more concerned with classical distributed ML than with FL, and they seldom explore how to apply decentralized FL to UAV networks. The authors of [119] addressed the issues with typical cloud-centric ML for UAV, such as privacy concerns, unacceptable latency, and resource load. To train a high-quality automated picture identification model, the authors of [120] developed an approach that depended on UAV swarms sharing ground-truth tagged data. Furthermore, they described a semisupervised FL (SSFL) for privacy-preserving UAV image identification. The proposed framework used Federated Mixing (FM) to enhance the naive combination of FL and semisupervised learning methods. Below, we discuss related work on FL-empowered UAV computing. **WPC and EH:**: UAVs can be used as an MEC server and an energy transmitter for terrestrial users [13; 18]. The optimization of a UAV's trajectory is discussed to improve performance in terms of computing resources [13], whereas the authors in [18] optimized the total required energy by a single UAV in the UAV-assisted wireless-powered MEC system by jointly optimizing the UAV's transmit power, CPU frequencies, trajectory, and offloading amount. The system, interestingly, used as MEC servers both the UAV and idle sensor devices that do not have computing tasks to offload tasks by other sensors. However, previous research on UAV-assisted Wireless-Powered Communication (WPC) [13; 121; 122; 123] focused on communication impacts and overlooked the potential of UAV-assisted WPC in FL-enabled networks. Furthermore, the authors of [124] investigated using Energy Harvesting (EH) from stochastic sources for FL. Moreover, the authors of [125] applied DRL to tackle a UAV-FL WPC network's long-term energy challenge. Several research papers suggest FL-based collaborative learning techniques, including UAVs. To the best of our knowledge, ref. [116] is the first work to suggest the use  
Figure 5: FL for UAV computing collaboration. of FL for joint power allocation and scheduling of UAV swarms. With data privacy restrictions becoming more severe, FL adoption can help collaborative learning build successful AI models without requiring the transmission of potentially sensitive raw data to a cloud server.