# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks  
Xuan-Toan Dang  
School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea; [EMAIL_ADDRESS] (X-T.D. ); [EMAIL_ADDRESS] (B-M.V. ); [EMAIL_ADDRESS] (Q.-S.N. ); [EMAIL_ADDRESS] (T.-T.-M.T. );[EMAIL_ADDRESS] (J.-S.E.) Federated learning (FL) offers a promising alternative by decentralizing the training process. FL technique operates by having a central server distribute global model parameters to remote devices, whereas each device uses its local dataset to update the model [11; 12]. Instead of transferring raw data, which is typically vast and sensitive, these devices send only their locally updated model parameters back to the server [13]. The server then aggregates these updates, refines the global model, and repeats the process until a desired level of accuracy is reached. This significantly reduces the amount of data exchanged over the network, improving communication efficiency [14]. The size of model updates is often much smaller than the raw data, which is particularly advantageous for reducing network traffic and latency. Furthermore, FL enhances privacy by keeping sensitive data on local devices, minimizing the risk of data theft or breaches during transmission [15; 16]. In this way, FL provides an efficient, scalable, and privacy-preserving solution for machine learning in distributed environments like IoT networks [17]. Although wired networks are known for their stability and reliability, wireless networks offer distinctive benefits that enhance the efficiency and scalability of FL, especially in the context of IoT [18]. Specifically, wireless networks allow devices to connect and disconnect seamlessly as they move in and out of coverage areas, which is essential for accommodating the vast and dynamic ecosystem of IoT devices. This adaptability ensures that new devices can be easily integrated into the FL process without significant infrastructure changes. Moreover, the use of wireless networks enables FL to reach remote or hard-to-wire locations and offers a diverse range of devices and datasets in the learning process [19]. This diversity can lead to the development of more generalized and robust models that perform better across diverse environments and use cases. Additionally, wireless protocols can be readily optimized for energy efficiency, ensuring battery-powered devices participate in FL without excessive energy consumption [20]. By leveraging the flexibility of wireless networks, FL can maximize the potential of distributed IoT devices, facilitating scalable and efficient machine learning in diverse, real-world environments, while preserving data privacy [18]. This powerful combination makes FL an optimal solution for training machine learning models in vast, dynamic, and geographically dispersed networks. Despite its advantages, deploying FL over wireless networks presents challenges, particularly in latency, reliability, scalability, and energy efficiency [21; 22; 23]. Among these challenges, the limited battery life of IoT devices emerges as a well-documented barrier to deploying FL effectively. To achieve high accuracy in the global model, service operators require IoT devices to engage in frequent local model training and update model parameters through wireless links. This process, while essential for improving the performance of the FL system, results in substantial energy consumption for UEs [21]. Moreover, as IoT devices are typically deployed in environments with limited access to charging infrastructure, the energy constraints become even more critical. Consequently, ensuring energy-efficient operation while maintaining the efficiency and effectiveness of the FL process is critical for practical FL implementation over wireless networks [24]. Existing surveys on FL primarily focus on technical optimizations, such as model compression, hyperparameter tuning, and training algorithm improvements.