期刊
ENTERPRISE INFORMATION SYSTEMS
卷 17, 期 9, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/17517575.2022.2101025
关键词
Federated learning; internet of things; privacy protection; encryption algorithm
This article focuses on the technical advantages, attack methods and classifications, as well as the distinctions between different encryption algorithms of federated learning. It also reviews the research on privacy protection and security mechanisms in federated learning, and identifies difficulties and opportunities.
Laws and regulations for privacy protection have been promulgated one after another, and the phenomenon of data islands has become a significant bottleneck hindering the development of big data and artificial intelligence technologies. From the perspective of the historical development, concepts, and architecture classification of federated learning, the technical advantages of federated learning are explained using Internet of Things. Simultaneously, numerous attack methods and classifications of federated learning systems are examined, as well as the distinctions between different federated learning encryption algorithms. Finally, it reviews research in the subject of federal learning privacy protection and security mechanisms, as well as identifies difficulties and opportunities.
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