A survey on federated learning: challenges and applications

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict priva...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 14; no. 2; pp. 513 - 535
Main Authors Wen, Jie, Zhang, Zhixia, Lan, Yang, Cui, Zhihua, Cai, Jianghui, Zhang, Wensheng
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Big Data
Communication
Complex Systems
Computational Intelligence
Control
Engineering
Federated learning
Heterogeneity
Machine learning
Mechatronics
Original
Original Article
Pattern Recognition
Privacy
Robotics
Security
Systems Biology
Personalized federated learning
Privacy protection
Federated learning
Machine learning
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Summary:Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL.
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ISSN:1868-8071
1868-808X
1868-808X
DOI:10.1007/s13042-022-01647-y