面向无线联邦学习模型压缩的多维资源联合优化研究

朱光照; 朱晓荣; 徐鼎

doi:10.11959/j.issn.2096-3750.2025.00391

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面向无线联邦学习模型压缩的多维资源联合优化研究

理论与技术 | 更新时间：2025-07-29

- 面向无线联邦学习模型压缩的多维资源联合优化研究
- Joint multi-dimensional resource optimization for model compression in wireless federated learning
- 物联网学报 2025年9卷第2期页码：190-201
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  [ "朱光照（2000‒ ），男，南京邮电大学通信与信息工程学院硕士生，主要研究方向为边缘计算、联邦学习。" ]
  [ "朱晓荣（1977‒ ），女，博士，南京邮电大学通信与信息工程学院教授、博士生导师，主要研究方向为5G/6G网络、智能物联网、网络大数据、区块链、群体智能等。" ]
  [ "徐鼎（1983‒ ），男，博士，南京邮电大学通信与信息工程学院副教授、硕士生导师，主要研究方向为无线网络边缘计算、无线通信非正交多址接入、无线通信物理层安全、天地一体化网络、物联网等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(92367102);江苏省重点研发计划项目(BE2020084-3;BE2021013-3)
- DOI：10.11959/j.issn.2096-3750.2025.00391
  中图分类号： TN929.5
- 收稿日期：2024-01-26，
  
  修回日期：2024-04-14，
  
  纸质出版日期：2025-06-10
- 稿件说明：
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朱光照,朱晓荣,徐鼎.面向无线联邦学习模型压缩的多维资源联合优化研究[J].物联网学报,2025,09(02):190-201.

ZHU Guangzhao,ZHU Xiaorong,XU Ding.Joint multi-dimensional resource optimization for model compression in wireless federated learning[J].Chinese Journal on Internet of Things,2025,09(02):190-201.
朱光照,朱晓荣,徐鼎.面向无线联邦学习模型压缩的多维资源联合优化研究[J].物联网学报,2025,09(02):190-201. DOI： 10.11959/j.issn.2096-3750.2025.00391.

ZHU Guangzhao,ZHU Xiaorong,XU Ding.Joint multi-dimensional resource optimization for model compression in wireless federated learning[J].Chinese Journal on Internet of Things,2025,09(02):190-201. DOI： 10.11959/j.issn.2096-3750.2025.00391.

摘要

针对边缘计算场景中，资源受限和网络动态的终端设备参与联邦学习产生的巨大时延和能耗问题，基于云-边-端三层联邦学习架构提出了一种高效训练和绿色节能的联邦学习算法。首先，将模型压缩技术引入三层联邦学习结构中，对三层联邦学习的模型收敛速率、训练时延和能耗进行理论分析。然后，根据理论分析结果进行问题建模，在一定的模型收敛速率下最小化全局模型训练时延和能耗，通过联合优化终端设备的发射功率、算力和模型压缩率，提高联邦学习的资源利用率。最后，将问题分解为3个优化子问题分别求解，设计了一种联合交替优化算法来获得原始问题的最优解。仿真结果表明，该算法可以适应大规模的边缘计算场景，在保证模型收敛速率的同时，与传统三层联邦学习算法相比，产生的时延和能耗分别减少了71.54%和48.76%，有效地降低了全局模型训练产生的时延和能耗。

Abstract

In the edge computing scenarios

resource-constrained and particiption of the dynamically terminal devices of network in federated learning cause high latency and high energy consumption. An efficient and environmentally friendly federated learning algorithm based on a three-tier cloud-edge-terminal architecture was proposed. Firstly

by introducing model compression techniques into the three-tier federated learning structure

a theoretical analysis was conducted on the model convergence rate

training latency

and energy consumption. Subsequently

based on the theoretical analysis

a problem was formulated to minimize the global model training latency and energy consumption under a certain model convergence rate by jointly optimizing the terminal devices' transmission power

computing power

and model compression rate. Finally

by decomposing the problem into three sub-optimization problems and solving them alternately

a joint alternating optimization algorithm was designed to obtain the optimal solution for the original problem. Experimental results demonstrate that the proposed algorithm is adaptable to large-scale edge computing scenarios. It achieves reductions of 71.54% and 48.76%

respectively

in latency and energy consumption compared with traditional three-layer federated learning algorithms

while ensuring the convergence rate of the model

and effectively reduces the latency and energy consumption generated by global model training.

关键词

Keywords

references

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