# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Non-Terrestrial-Based Architectures
#### 3.2.3 CFL in Satellite Communication Networks  
Satellite-based federated learning (SFL) provides global coverage and high scalability, making it ideal for large-scale operations in remote or underserved areas. Satellites excel in fault tolerance and offer persistent connectivity, overcoming limitations such as the restricted battery life of UAVs and the limited coverage of HAPSs. Unlike these platforms, satellites are not constrained by energy limitations or geographical boundaries, makingthem the most reliable choice for wide-area communication [95]. SFL is particularly valuable in applications like disaster response, environmental monitoring, and secure global communication, where real-time data processing and privacy are paramount [96]. In a typical SFL setup, multiple satellites gather data from edge devices while a central server, located on Earth or in orbit, aggregates and processes the model updates [97], as illustrated in Figure 5a. This system enables distributed learning across satellites and ground stations, with each satellite or device conducting local computations and sharing updates with the central server.  
Energy efficiency is a cornerstone of SFL due to the power constraints of satellites, which rely on limited onboard energy sources. Various strategies have been proposed to address this challenge, tailored to specific use cases. For low-power IoT devices in LEO satellite systems, [98] proposed selective device participation to minimize energy consumption. By balancing computational and communication loads, the approach aligns with power-aware scheduling principles and leverages dynamic resource allocation to enhance efficiency. The authors in [99] suggested satellite grouping and the use of inter-satellite links (ISLs) to address data heterogeneity and communication challenges. This strategy includes caching frequently accessed data onboard satellites and optimizing transmission power and computing frequency, effectively reducing redundant transmissions and conserving energy. Focusing on satellite-terrestrial integration, Ref. [100] proposed caching mechanisms to offload traffic from terrestrial networks, improving energy efficiency. By storing frequently accessed data onboard satellite, energy consumption is significantly reduced, aligning with conservation principles. All the studies focused on optimizing energy consumption while preserving system performance, employing techniques like dynamic resource management, caching, and ISL optimization as key strategies.  
Energy efficiency is further enhanced through hybrid systems that combine satellites, UAVs, and HAPSs in FL [101; 102]. UAVs are well suited for localized tasks due to their proximity to the ground, while HAPSs and satellites offer broad, continuous coverage for large-scale operations. This complementary arrangement highlights the potential for collaborative frameworks that balance localized and global learning tasks. In particular, the integration of UAVs and HAPSs with FL has garnered significant attention for leveraging their nonterrestrial network capabilities to provide dynamic, scalable, and energy-efficient communication platforms, especially in environments with limited terrestrial infrastructure.  
Figure 5: FL strategies over a satellite communication network