Chinese Journal of Network and Information Security ›› 2021, Vol. 7 ›› Issue (5): 77-92.doi: 10.11959/j.issn.2096-109x.2021056

Special Issue: 联邦学习

• TopicⅡ: Machine Learning and Security Application • Previous Articles     Next Articles

Survey of federated learning research

Chuanxin ZHOU, Yi SUN, Degang WANG, Huawei GE   

  1. Information Engineering University, Zhenghzou 450001, China
  • Revised:2020-10-10 Online:2021-10-15 Published:2021-10-01
  • Supported by:
    The National Natural Science Foundation of China(61702550)

Abstract:

Federated learning has rapidly become a research hotspot in the field of security machine learning in recent years because it can train the global optimal model collaboratively without the need for multiple data source aggregation.Firstly, the federated learning framework, algorithm principle and classification were summarized.Then, the main threats and challenges it faced, were analysed indepth the comparative analysis of typical research programs in the three directions of communication efficiency, privacy and security, trust and incentive mechanism was focused on, and their advantages and disadvantages were pointed out.Finally, Combined with application of edge computing, blockchain, 5G and other emerging technologies to federated learning, its future development prospects and research hotspots was prospected.

Key words: federated learning, privacy protection, blockchain, edge of computing

CLC Number: 

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