Content IntroductionAbstract:With the rapid development of fifth-generation network technology and the Internet of Things, the number of end-user devices and various applications is surging, resulting in a massive amount of data generated at the network edge. To efficiently process this data, innovative mobile edge computing frameworks have emerged to achieve low latency and efficient computing capabilities close to user traffic. In recent years, due to its privacy-preserving advantages, federated learning has shown empirical success in edge computing. Thus, it has become a promising solution for analyzing and processing distributed data in various machine learning tasks, which are the main workloads in mobile edge computing. Unfortunately, end-user devices are typically powered by batteries with limited capacity, facing challenges when performing energy-intensive federated learning tasks. To address these challenges, many energy-saving strategies have been proposed. Considering the current lack of comprehensive summaries and classifications of these strategies, we conducted a thorough investigation of the latest advancements in energy-saving strategies for federated learning in mobile edge computing. Specifically, we first introduce the system model and energy consumption model in federated learning, involving computation and communication. Then, we analyze the challenges in improving energy efficiency and summarize energy-saving strategies from three perspectives: learning-based strategies, resource allocation strategies, and client selection strategies. A detailed analysis of these strategies is provided, comparing their advantages and disadvantages. Moreover, we visually demonstrate the impact of these strategies on federated learning performance by presenting experimental results. Finally, we discuss several potential research directions for energy-efficient federated learning.Keywords:Mobile Edge Computing;Federated Learning;Energy EfficiencyAuthors:
| Kang YAN1, Nina SHU1 , Tao WU1 , Chunsheng LIU1, Panlong YANG2
Affiliation: 1College of Electronic Countermeasures, National University of Defense Technology, Hefei, 230009, China 2School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China
Citation Format: Kang YAN, Nina SHU, Tao WU, Chunsheng LIU, Panlong YANG, 2024. A survey of energy-efficient strategies for federated learning in mobile edge computing. Frontiers of Information Technology & Electronic Engineering, 25(5):645-663.https://doi.org/10.1631/FITEE.2300181 |
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Frontiers of Information Technology & Electronic Engineering (abbreviated as FITEE, Chinese name “Information and Electronic Engineering Frontiers (English)”, ISSN 2095-9184, CN 33-1389/TP) is a comprehensive English academic monthly journal in the field of information electronics, included in SCI-E and EI, latest impact factor 3.0, located in JCR Q2 area. It was formerly known as the “Journal of Zhejiang University English Edition C: Computer and Electronics” established in 2010, and was renamed in 2015. It is now the sub-journal of the Chinese Academy of Engineering’s journal in the field of information and electronic engineering. It covers fields such as computer science, information and communication, control, electronics, and optics. Article types include research papers, reviews, personal viewpoints, and commentaries. The current editor-in-chief is Academician Pan Yunhe of the Chinese Academy of Engineering and Fei Aiguo. It implements an international peer review system, and initial feedback is generally provided within 2-3 months. Once accepted, articles will be published online quickly.
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