Full Text:   <415>

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CLC number: TN929.5

On-line Access: 2024-06-04

Received: 2023-03-14

Revision Accepted: 2024-06-04

Crosschecked: 2023-09-14

Cited: 0

Clicked: 428

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Kang YAN

https://orcid.org/0000-0002-0258-0817

Tao WU

https://orcid.org/0000-0003-1344-835X

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.645-663

http://doi.org/10.1631/FITEE.2300181


A survey of energy-efficient strategies for federated learning in mobile edge computing


Author(s):  Kang YAN, Nina SHU, Tao WU, Chunsheng LIU, Panlong YANG

Affiliation(s):  School of Electronic Engineering, National University of Defense Technology, Hefei 230009, China; more

Corresponding email(s):   wutao20@nudt.edu.cn

Key Words:  Mobile edge computing, Federated learning, Energy-efficient


Kang YAN, Nina SHU, Tao WU, Chunsheng LIU, Panlong YANG. A survey of energy-efficient strategies for federated learning in mobile edge computing[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 645-663.

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Abstract: 
With the booming development of fifth-generation network technology and Internet of Things, the number of end-user devices (EDs) and diverse applications is surging, resulting in massive data generated at the edge of networks. To process these data efficiently, the innovative mobile edge computing (MEC) framework has emerged to guarantee low latency and enable efficient computing close to the user traffic. Recently, federated learning (FL) has demonstrated its empirical success in edge computing due to its privacy-preserving advantages. Thus, it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks, which are the major workloads in MEC. Unfortunately, EDs are typically powered by batteries with limited capacity, which brings challenges when performing energy-intensive FL tasks. To address these challenges, many strategies have been proposed to save energy in FL. Considering the absence of a survey that thoroughly summarizes and classifies these strategies, in this paper, we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in MEC. Specifically, we first introduce the system model and energy consumption models in FL, in terms of computation and communication. Then we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives: learning-based, resource allocation, and client selection. We conduct a detailed analysis of these strategies, comparing their advantages and disadvantages. Additionally, we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental results. Finally, several potential future research directions for energy-efficient FL are discussed.

移动边缘计算中联邦学习的能效策略综述

颜康1,束妮娜1,吴韬1,2,刘春生1,杨盘隆3
1国防科技大学电子对抗学院,中国合肥市,230009
2香港理工大学电子计算学系,中国香港特别行政区,999077
3中国科学技术大学计算机科学与技术学院,中国合肥市,230026
摘要:随着第五代网络技术和物联网的蓬勃发展,终端用户设备数量和各种各样的应用程序正在激增,从而在网络边缘产生大量数据。为了高效处理这些数据,创新性的移动边缘计算框架已经出现,以实现靠近用户流量的低延迟和高效的计算能力。近年来,由于其保护隐私的优势,联邦学习在边缘计算中展示出经验性的成功。因此,它成为各种机器学习任务中分析和处理分布式数据有前景的解决方案,这些任务是移动边缘计算中的主要工作负载。遗憾的是,终端用户设备通常由容量有限的电池供电,在执行高能耗的联邦学习任务时会面临挑战。为应对这些挑战,已有许多节能策略被提出。考虑到目前缺乏全面总结和分类这些策略的调查,我们对移动边缘计算中联邦学习的节能策略的最新进展作了全面调查。具体而言,首先介绍了联邦学习中的系统模型和能耗模型,涉及计算和通信。然后,分析了提高能效方面的挑战,并从3个角度总结了节能策略:基于学习的策略、资源分配策略和客户端选择策略。对这些策略作了详细分析,比较了它们的优势和劣势。此外,通过展示实验结果,直观展现了这些策略对联邦学习性能的影响。最后,讨论了节能联邦学习若干潜在研究方向。

关键词:移动边缘计算;联邦学习;能量高效

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