## 1 Introduction
### Related Work
The authors of [24] formulated uncertainty minimization as a reward maximization problem and proposed a deep Q-network to optimize task offloading and flying orientation, where different parameters affect the search performance [24]. Another approach transferred wireless power from towers to UAVs and minimized computation overheads; less data led to overfitting. Providing joint power charging between towers and UAVs using workload-aware scheduling algorithms a solution approach was proposed based on MDP to handle workload-aware scheduling decisions [23]. For a dynamic wireless sensor network that employs orthogonal frequency division multiplexing (OFDM), a UAV trajectory design problem is investigated in [61]. A similar approach reduced delay on edge computing networks by reducing dynamic variability in task arrival [62]. In [63], a reinforcement learning (RL)-based cooperative navigation algorithm is proposed to plan efficient paths to avoid collisions. For multi-UAV cooperative navigation, a prioritized experience replay strategy was designed by considering both temporal difference errors and node connectivity [63]. By optimizing location and velocity at each time slot, a UAV was trained to navigate faster [64]. Multi-agent communication explores relationships between message entropy and bandwidth and introduces mutual information to approximate message entropy [64]. Using hierarchical federated learning (HFL), clusters of UAVs were divided into cluster heads [65]. With content caching, vehicles' context information can be used to allow UAVs to cache popular content, where clusters of UAVs are formed based on vehicles' mobility patterns [66]. In another approach, using adaptive FRL, the UAV energy consumption is calibrated to determine the local update frequency in [67]. Other work aimed to optimize network environments and fluctuations in data traffic, and developed an algorithm to select a UAV as a central server [68]. The authors of [69] developed an approach with multiple objectives for UAVs with power constraints [69]. An optimization problem for dynamic data caching and computation offloading to reduce the average processing delay and maximize cache hits for UAVs was formulated in [70]. An optimization problem was constructed to minimize FL convergence time considering constraints such as uplink latency, selection strategy, local model training latency, and energy consumption in [71]. The authors of [72] optimized the position of UAVs based on the instantaneous signal-to-interference noise ratio (SINR). The resource allocation strategy improved the FL execution by 66.67% and attained lower latency and energy consumed compared to other resource allocation techniques [72]. The authors of [73] enhance the generalization capability of routing decisions using FL where domain controllers and servers communicate. The proposed method facilitates knowledge transfer, accelerates training, and reduces communication costs [73]. The authors of [74] integrate FL with imitation learning to coordinate UAVs' maneuvers. The authors remove biased estimations of imitation parameters using generative adversarial imitation learning. The work aims to regularize the federated gradient updates to achieve efficient parameter interactions and coordinated swarm policies using self-imitated learning [74].