A survey on federated learning: challenges and applications

【Author】 Wen, Jie; Zhang, Zhixia; Lan, Yang; Cui, Zhihua; Cai, Jianghui; Zhang, Wensheng

【Source】INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS

【影响因子】4.377

【Abstract】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.

【Keywords】Federated learning; Machine learning; Privacy protection; Personalized federated learning

【发表时间】

【收录时间】2022-11-28

【文献类型】综述

【主题类别】

区块链技术-协同技术-联邦学习