移动通信2024，Vol.48Issue(12)：122-128,7.DOI:10.3969/j.issn.1006-1010.20240730-0002
边缘计算网络中联邦学习算法设计与优化
Design and Optimization of Federated Learning Algorithms in Edge Computing Networks
陈柱 1莫俊彬 1陈青霞 1叶绍雄 1郭俊滨1
作者信息
- 1. 中国联合网络通信有限公司清远市分公司,广东 清远 511500
- 折叠
摘要
Abstract
Aiming at the bottleneck of network resources and data security problems faced by massive IoT terminals'training,this paper proposes the design and optimization of federated learning algorithms in edge computing networks.Firstly,an end-edge-cloud three-layer federated learning architecture is designed to achieve efficient federated learning.The device side is responsible for feature extraction,while the edge and cloud sides are responsible for model parameter training and integration,respectively.Then,model compression techniques are introduced to construct communication and energy consumption models and to model the federated learning problem,minimizing the latency and energy consumption of cloud-based model training.Finally,a particle swarm optimization method is designed to obtain the optimal solution of the original problem,achieve fast convergence of the algorithm,and to some extent reduce the energy consumption and latency of federated learning algorithms.Simulation results show that compared to other algorithms,the proposed algorithm can be adapted to large-scale IoT scenarios with the ensurance of the convergence rate of the algorithm,while reducing energy consumption and latency to a certain extent.关键词
边缘计算网络/联邦学习/通信模型/能耗模型Key words
edge computing network/federated learning/communication model/energy consumption model分类
信息技术与安全科学引用本文复制引用
陈柱,莫俊彬,陈青霞,叶绍雄,郭俊滨..边缘计算网络中联邦学习算法设计与优化[J].移动通信,2024,48(12):122-128,7.
