移动边缘计算中联邦学习的能效策略综述

颜康; 束妮娜; 吴韬; 刘春生; 杨盘隆

doi:10.1631/FITEE.2300181

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移动边缘计算中联邦学习的能效策略综述

专题 | Updated：2024-06-03

- 移动边缘计算中联邦学习的能效策略综述
- A survey of energy-efficient strategies for federated learning in mobile edge computing
- “在移动边缘计算（MEC）领域，与联邦学习（FL）的集成是一个重大进步，解决了在最终用户设备（ED）上处理数据的隐私和效率问题。然而，电池供电的ED的能量限制对FL任务构成了重大挑战。本文对MEC中FL的节能策略进行了广泛的调查，从基于学习、资源分配到客户选择的角度考察了系统模型、能耗和策略。它提供了详细的分析、实验结果和潜在的未来研究方向，为MEC环境中更可持续、更高效的FL实施铺平了道路。”
- 信息与电子工程前沿（英文） 2024年25卷第5期页码：645-663
- Affiliations：
  
  1.School of Electronic Engineering, National University of Defense Technology, Hefei 230009, China
  2.Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
  3.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
- Author bio：
  
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(62002377;62072303;62072424;61872178;62272223);Hong Kong Scholars Program(2021-101);High-Level Talent Fund(22-TDRCJH-02-013)
- DOI：10.1631/FITEE.2300181
  中图分类号： TN929.5
- 收稿：2023-03-14，
  
  录用：2023-09-14，
  
  纸质出版：2024-05-0
- Accepted：
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颜康, 束妮娜, 吴韬, 等. 移动边缘计算中联邦学习的能效策略综述[J]. 信息与电子工程前沿（英文）, 2024,25(5):645-663.

Kang YAN, Nina SHU, Tao WU, et al. 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.
颜康, 束妮娜, 吴韬, 等. 移动边缘计算中联邦学习的能效策略综述[J]. 信息与电子工程前沿（英文）, 2024,25(5):645-663. DOI： 10.1631/FITEE.2300181.

Kang YAN, Nina SHU, Tao WU, et al. 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. DOI： 10.1631/FITEE.2300181.

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移动边缘计算中联邦学习的能效策略综述

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

DOI：10.1631/FITEE.2300181