基于层析分析改进的联邦平均算法

doi:10.11896/jsjkx.201000093

计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 32-40.doi: 10.11896/jsjkx.201000093

• 数据库&大数据&数据科学* • 上一篇下一篇

基于层析分析改进的联邦平均算法

罗长银^1,2,3, 陈学斌^1,2,3, 马春地¹, 张淑芬^1,2,3

1 华北理工大学理学院河北唐山 063210
2 华北理工大学河北省数据科学与应用重点实验室河北唐山063210
3 华北理工大学唐山市数据科学重点实验室河北唐山063210

收稿日期:2020-08-14 修回日期:2021-01-03 发布日期:2021-08-10
通讯作者: 陈学斌(chxb@qq.com)
基金资助:
国家自然科学基金项目(61572170,61170254);唐山市科技项目(18120203A)

Improved Federated Average Algorithm Based on Tomographic Analysis

LUO Chang-yin^1,2,3, CHEN Xue-bin^1,2,3, MA Chun-di¹, ZHANG Shu-fen^1,2,3

1 College of Science,North China University of Science and Technology,Tangshan,Hebei 063210,China;
2 Hebei Province Key Laboratory of Data Science and Application,North China University of Science and Technology,Tangshan,Hebei 063210,China;
3 Tangshan Data Science Key Laboratory,North China University of Science and Technology,Tangshan,Hebei 063210,China

Received:2020-08-14 Revised:2021-01-03 Published:2021-08-10
About author:LUO Chang-yin,born in 1994,master,is a member of China Computer Federation.His main research interest include data security and so on.(1394301218@qq.com)CHEN Xue-bin,born in 1970,professor,Ph.D,is a distinguished member of China Computer Federation.His main research interest include data security,Internet of things security and network security.
Supported by:
National Natural Science Foundation of China(61572170,61170254) and Tangshan Science and Technology Project(18120203A).

摘要/Abstract

摘要： 联邦平均(Fedavg)算法采用权重更新来更新全局模型,该算法在权重更新时仅考虑每个客户端数据量的大小,未考虑数据质量对模型的影响。针对该问题,文中提出了基于层次分析改进的联邦平均算法,首次从数据质量的角度来处理多源数据。首先采用熵权法计算数据中各属性的重要度,并将其作为层次分析中准则层的数值,计算每个客户端数据的质量,然后结合客户端数据量的大小,重新计算全局模型中的权重。仿真实验的结果表明,对于中小型数据集而言,使用支持向量机训练的模型准确度最高,达到了85.715 2%;对于大型数据集而言,采用随机森林训练的模型准确率最高,达到了91.932 1%。与传统联邦平均方法相比,所提方法在中小数据集上准确率提升了3.5%,在大数据集上提升了1.3%,能够在提升模型准确率的同时提高数据与模型的安全性。

关键词: 层析分析, 联邦平均(Fedavg), 权重更新, 熵权法

Abstract: In the federated average algorithm,the weight update is used to update the global model.The algorithm only considers the size of the data volume of each client when the weight is updated,and does not consider the impact of data quality on the mo-del.An improvement based on analytic hierarchy is proposed.The federated averaging algorithm is the first to process multi-source data from the perspective of data quality.First,the entropy method is used to calculate the importance of each attribute in the data,and it is used as the value of the criterion layer in the level analysis to calculate the data of each client quality.Then,combined with the amount of data on the client,the weight update method is recalculated in the global model.The simulation results show that for small and medium data sets,the model trained with support vector machines has the highest accuracy,rea-ching 85.7152%.For large data sets,the model trained with random forest has the highest accuracy,reaching 91.9321%.Compared with the traditional federal average method,the accuracy rate is increased by 3.5% on small and medium data sets and 1.3% on large data sets,which can improve the accuracy of the model while improving the security of the data and model.

Key words: Entropy weight method, Federated average(Fedavg), Tomographic analysis, Weight update

中图分类号:

TP391

罗长银, 陈学斌, 马春地, 张淑芬. 基于层析分析改进的联邦平均算法[J]. 计算机科学, 2021, 48(8): 32-40. https://doi.org/10.11896/jsjkx.201000093

LUO Chang-yin, CHEN Xue-bin, MA Chun-di, ZHANG Shu-fen. Improved Federated Average Algorithm Based on Tomographic Analysis[J]. Computer Science, 2021, 48(8): 32-40. https://doi.org/10.11896/jsjkx.201000093

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基于层析分析改进的联邦平均算法

Improved Federated Average Algorithm Based on Tomographic Analysis

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