# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL  
### Terrestrial-Based Architectures  
#### 3.1.1 CFL in Centralized Wireless Networks  
Centralized wireless networks, illustrated in Figure 2, are systems where a central entity (e.g., a BS, access point, or central server) oversees resource management and operation decision-making. The central controller manages communication tasks, including user access, data routing, resource allocation, and security, providing stable connectivity for edge devices. Figure 2: CFL over a centralized wireless network. As the adoption of machine learning and deep learning grows within centralized wireless networks, there is an increasing demand for privacy-preserving and resource-efficient methods. Transmitting raw data to a central server often proves impractical due to bandwidth and privacy constraints. However, the structure of centralized networks aligns well with CFL, enabling decentralized training of machine learning models without sharing raw data [18; 39; 40; 41; 42]. * CFL in Cellular Networks: A cellular network is a type of centralized system, providing wireless communication services over wide geographical areas. Several studies on energy-efficient CFL in cellular networks were presented in [21; 39; 43; 44; 45; 46]. In [39], the authors examined strategies to improve overall energy efficiency in CFL over 5G+ mobile devices by analyzing energy consumption trade-offs between local computation and wireless communication. Building on this, [PERSON]. [45] introduced a joint design for wireless transmission and weight quantization to enhance energy efficiency on mobile devices. Similarly, [PERSON]. [21] tackled energy-efficient transmission challenges for CFL in wireless networks. * CFL in IoT Network: Centralized network architectures have been widely applied in IoT networks to manage large-scale deployments, optimize communication, and streamline data processing. With the rapid proliferation of IoT devices, coupled with the integration of machine learning in IoT applications, CFL has emerged as a promising solution to address these challenges [11; 47; 48; 49]. In [48], the author proposed a multiagent federated reinforcement learning algorithm to optimize energy-efficient CFL while ensuring the quality of service (QoS) for IoT nodes, fairness, and UAV trajectory planning. The authors in [49] explored resource allocation and energy-efficient CFL schemes for relay-assisted IoT networks, focusing on minimizing the overall energy consumption of IoT devices while meeting latency constraints for training and transmission. * CFL in Fog-Cloud RAN: Fog-cloud radio access networks (F-RAN or C-RAN) represent another centralized architecture, especially for 5G and beyond. Energy-efficient CFL in such networks is highly promising [50; 51]. The authors in [51] developed an energy-efficient CFL framework for C-RAN with strict energy constraints. * Resource Allocation for CFL: Resource allocation plays a critical role in CFL-based systems, as it directly impacts communication efficiency, energy efficiency, model convergence speed, and overall system performance. By strategically managing resources like bandwidth, power, and computational capacity, CFL can operate more effectively in centralized networks [52; 53; 54; 55; 56; 57]. Ref. [55] incorporated a simultaneous wireless information and power transfer (SWIPT) system with multicarrier NOMA to improve CFL energy efficiency. In [56], the authors proposed an energy-efficient CFL framework for wireless networks by employing a joint computation-and-communication resource management strategy. Leveraging intelligent reflecting surfaces (IRS), Ref. [57] optimized resource allocation for CFL in centralized wireless networks. * MIMO/mMIMO Technology for CFL: CFL has been applied to enhance performance in large-scale cellular and IoT networks.