# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Non-Terrestrial-Based Architectures
#### 3.2.1 CFL in UAV Communication Networks  
UAVs are highly adaptable platforms that support FL training by collecting and aggregating data from devices across diverse locations. With their dynamic mobility and ability to fly autonomously, UAVs are particularly well-suited for scenarios requiring on-the-fly connectivity. Their ability to access remote and hard-to-reach areas makes them essential for CFL tasks where localized data needs collaborative processing, such as in agriculture monitoring, disaster management, and environmental sensing. However, the energy-constrained nature of UAVs necessitates careful planning to balance efficient flight operations with CFL communication demands. This subsection reviews existing research on UAV-based CFL and highlights strategies to optimize energy use while maintaining system performance. One of the first CFL frameworks within UAV networks, as illustrated in Figure 4a, was introduced in [73]. This study analyzed the impact of wireless factors, such as fading, transmission delay, and UAV antenna deviations, on CFL convergence. It proposed a joint power allocation and scheduling strategy to optimize the convergence rate while accounting for energy consumption and system delay constraints. Similarly, the work in [74] provided an overview of FL applications in UAV-enabled wireless networks, emphasizing the suitability of FL to address various challenges. Optimization of the placement and movement of UAVs is critical to achieving high communication quality and energy efficiency, as evidenced by numerous studies. For instance, research in [75; 76; 77; 78; 79; 80] introduced various strategies incorporating UAV placement optimization. A novel approach in [75] proposed deploying UAVs with edge computing capabilities, functioning as both aerial energy sources and servers for FL aggregation. Using a block decomposition method, the study demonstrated the energy efficiency of this solution compared with benchmarks. Similar to the work in [76], Ref. [78] addressed joint UAV location and resource allocation optimization to reduce energy consumption in air-ground integrated FL systems. Meanwhile, Ref. [77] proposed a hybrid split and federated learning framework for UAV networks, allowing users to choose between split and federated training. To further improve energy efficiency while maintaining performance, a user scheduling algorithm was developed within this framework. Regarding UAV trajectory, the investigation in [81] addressed energy efficiency optimization for FL in UAV communication networks, proposing an alternating optimization approach to solve the nonconvex problem and evaluate UAV trajectories for better performance. The study in [82] explored the concept of covert federated learning in UAV-enabled networks, ensuring FL operations remain hidden from adversarial surveillance. This framework optimized UAV trajectory, jamming power, and communication scheduling to achieve covert learning while maintaining model convergence. Efficient resource allocation remains a critical challenge for energy-efficient UAV-FL. In addition to the articles previously mentioned [75; 76; 77; 78; 79; 80], the study in [83] presented a multiagent federated reinforcement learning algorithm for resource management in UAV-assisted mobile edge computing (MEC) systems. By tackling power control and user association, the algorithm demonstrated comparable performance to centralized schemes while enhancing operational efficiency and ensuring privacy protection. Although the results appear promising, further research is necessary to completely comprehend the implications of this algorithm in various contexts. The study in [84] addressed battery-constrained FL in UAV-enabled IoT networks, optimizing energy consumption and latency using a deep deterministic policy gradient-based algorithm, prolonging UAV battery life, and ensuring uninterrupted FL training.