A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks

Xuan-Toan Dang, Binh-Minh Vu, Quynh-Suong Nguyen, Thi-Thuy-Minh Tran, Joon-Soo Eom and Oh-Soon Shin ()
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Xuan-Toan Dang: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
Binh-Minh Vu: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
Quynh-Suong Nguyen: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
Thi-Thuy-Minh Tran: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
Joon-Soo Eom: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
Oh-Soon Shin: School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea

Energies, 2024, vol. 17, issue 24, 1-28

Abstract: Federated learning (FL) has emerged as a decentralized, cutting-edge framework for training models across distributed devices, such as smartphones, IoT devices, and local servers while preserving data privacy and security. FL allows devices to collaboratively learn from shared models without exchanging sensitive data, significantly reducing privacy risks. With these benefits, the deployment of FL over wireless communication systems has gained substantial attention in recent years. However, implementing FL in wireless environments poses significant challenges due to the unpredictable and fluctuating nature of wireless channels. In particular, the limited energy resources of mobile and IoT devices, many of which operate on constrained battery power, make energy management a critical concern. Optimizing energy efficiency is therefore crucial for the successful deployment of FL in wireless networks. However, existing reviews on FL predominantly focus on framework design, wireless communication, and security/privacy concerns, while paying limited attention to the system’s energy consumption. To bridge this gap, this article delves into the foundational principles of FL and highlights energy-efficient strategies tailored for various wireless architectures. It provides a comprehensive overview of FL principles and introduces energy-efficient designs, including resource allocation techniques and communication architectures, tailored to address the unique challenges of wireless communications. Furthermore, we explore emerging technologies aimed at enhancing energy efficiency and discuss future challenges and opportunities for continued research in this field.

Keywords: federated learning (FL); decentralize learning; energy efficiency; wireless network; internet of things (IoT) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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