# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Non-Terrestrial-Based Architectures
#### 3.2.2 CFL over HAPS Communication Networks  
HAPSs, positioned 20 to 50 km above Earth, offer extensive coverage and reliable line-of-sight (LOS) communication [86], making them an ideal candidate for integrating CFL into aerial networks, as depicted in Figure 4b. Their stable positioning, broad connectivity, and ability to act as mega-cells are particularly valuable for 6G networks, addressing energy efficiency and latency sensitivity in CFL. This subsection explores HAPS-enabled CFL architectures, focusing on energy-efficient resource management, hierarchical model optimization, and integration with other aerial platforms to boost CFL energy efficiency.  
Energy efficiency is vital for enhancing HAPS performance in CFL scenarios. The research in [87] underscores dynamic resource allocation strategies tailored to the fluctuating demands of HAPS-enabled CFL networks. Similarly, Ref. [88] delves into CFL techniques for task and resource allocation in wireless high-altitude balloon networks, which are equally relevant to HAPSs. The study proposed a support vector machine (SVM)-based CFL algorithm to minimize energy and time consumption. These studies underscore HAPSs' potential for sustainable CFL operations while acknowledging the complexity and need for further optimization research.  
The synergy between HAPSs and UAVs enhances CFL frameworks by improving data aggregation and model training efficiency. For instance, [PERSON]. [87] outlined a collaborative framework integrating HAPSs and UAVs, while [89] introduced a hierarchical aerial computing framework that leverages these platforms for efficient IoT data processing. Privacy-preserving CFL is another avenue explored in [90], where a federated deep reinforcement learning model is used for HAPS-supported multi-UAV deployment. In this architecture, the HAPS aggregates model updates from various UAVs operating autonomously, reducing data transmission and preserving privacy. The collaboration between HAPSs and UAVs demonstrates their combined effectiveness in enhancing energy efficiency within CFL settings.  
The integration of HAPSs with other aerial platforms, such as LEO satellites, presents additional opportunities for energy optimization in CFL systems. For example, [PERSON]. [91] introduced a sparse channel estimation scheme tailored for CFL in nonterrestrial networks (NTNs), including HAPSs. This approach enhances channel estimation accuracy, crucial for efficient uplink and downlink CFL parameter transmission, significantly improving spectral and energy efficiency. By leveraging HAPS as a central node in NTNs, CFL systems benefit from more consistent performance, especially in dynamic and high-mobility scenarios.  
HAPSs also play a critical role in air-ground integration for CFL in intelligent transport systems and edge intelligence networks. [PERSON]. [92; 93] highlighted HAPSs' ability to combine aerial and terrestrial networks, distributing CFL processes efficiently across 3D networking architectures. Through proper network selection and optimal offloading, these platforms reduce latency and energy consumption in vehicle-to-everything and IoT applications, demonstrating their potential to empower edge intelligence and streamline communication between network nodes.  
Integrating HAPSs into CFL architectures boosts energy efficiency by centralizing aggregation and coordinating resources across UAV and LEO layers, reducing device energy consumption and enabling scalable CFL. Future research could explore hybrid aerial systems combining UAVs and HAPSs with energy-harvesting technologies to extend operation in energy-constrained environments. Additionally, the development of CFL protocols tailored for aerial networks could further optimize energy usage and processing speed, enhancing resilience and adaptability [94].