电子学报2023，Vol.51Issue(11)：3061-3069,9.DOI:10.12263/DZXB.20230504
基于边缘计算的环境监测自适应联邦学习算法
Federated Learning Scheme for Environmental Monitoring Based on Edge Computing
摘要
Abstract
Aiming at the problems of unbalanced edge device resources,communication delay and low model quality in the field of environmental monitoring,this paper proposes an adaptive federated learning algorithm for environmental monitoring based on edge computing.This algorithm aims to use edge devices for data processing,and according to each the resource limitation of the device adjusts the aggregation frequency of the global model to better adapt to different moni-toring environments.By considering the resource differences between edge devices,the algorithm adopts a strategy of dy-namically optimizing the iteration frequency to improve the training effect of the model.Compared with the traditional fixed iteration frequency,the adjustment strategy of this algorithm is more flexible and can better adapt to different data dis-tribution and participant characteristics.Through a large number of experimental evaluations,and using the same algorithm convolutional neural networks-federated learning(CNN-FL),federated averaging(FedAvg)and hierarchical federated edge learning(HFEL),the algorithm proposed in this paper has significant advantages in algorithm performance and economic cost.This algorithm provides an efficient,safe and reliable method for environmental monitoring.Expanded approach to data analysis and modeling to help drive improvements in environmental monitoring capabilities.关键词
环境监测/自适应联邦学习/边缘计算/模型聚合/优化算法Key words
environmental monitoring/adaptive federated learning/edge computing/model polymerization/optimi-zation algorithm分类
信息技术与安全科学引用本文复制引用
蒋伟进,韩裕清,吴玉庭,周为,陈艺琳,王海娟..基于边缘计算的环境监测自适应联邦学习算法[J].电子学报,2023,51(11):3061-3069,9.基金项目
国家自然科学基金(No.61772196,No.72088101) （No.61772196,No.72088101）
湖南省自然科学基金(No.2020JJ4249)National Natural Science Foundation of China(No.61772196,No.72088101) （No.2020JJ4249）
Hunan Provincial Natural Science Foundation(No.2020JJ4249) （No.2020JJ4249）
