期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 149, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2020.106854
关键词
Federated learning; Literature review; Citation analysis; Research front
资金
- National Key R&D Program of China [2018YFE0105000, 2018YFB1305304]
- National Natural Science Foundation of China [51475334]
- Shanghai Municipal Commission of science and technology [19511132100]
Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to overcome challenges of data silos and data sensibility. Exactly what research is carrying the research momentum forward is a question of interest to research communities as well as industrial engineering. This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL. This study aims to review prevailing application in industrial engineering to guide for the future landing application. This study also identifies six research fronts to address FL literature and help advance our understanding of FL for future optimization. This study contributes to conclude application in industrial engineering and computer science and summarize a review of applications in FL.
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