Overview of federated learning: Technology, applications and future

被引：0
|
作者：
Li S. [1 ,2 ]
Yang L. [2 ]
Li C. [1 ,2 ]
Zhang A. [1 ,2 ]
Luo R. [2 ]
机构：
[1] State Key Laboratory of Public Big Data, Guixhou University, Guiyang
[2] School of Mechanical Kngineering, Guizhou University, Guiyang
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 07期
关键词：
communication efficiency; federated learning; heterogeneity; incentive mechanism; privacy protection;
D O I：
10.13196/j.cims.2022.07.018
中图分类号：
学科分类号：
摘要：
Federated Learning (FL) is driven by multi-party data participation, and it maximizes the value of the data itself through data encryption interaction. In recent years, FL has attracted extensive attention from researchers from all walks of life and gradually moved from basic theoretical research to practical applications, which provides new technologies for further exploiting the value of data for enterprises. Based on the definition and classification of FL, a comprehensive analysis and summary of the research progress of related technologies at home and abroad was conducted, including privacy protection, communication efficiency, heterogeneity, and incentive mechanisms. The current application platforms and frameworks of FL were introduced, and the application frameworks of FL was proposed in the fields of intelligent manufacturing, medical treatment and education. Combined with the deficiencies of FL in some key open issues, its future development trends and directions were summarized for providing a reference for the theoretical research and applications of FL. © 2022 CIMS. All rights reserved.
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页码：2119 / 2138
页数：19
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