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A review of applications in federated learning

Li, Li ; Fan, Yuxi ; Tse, Mike ; Lin, Kuo-Yi

Computers & industrial engineering, 2020-11, Vol.149, p.106854, Article 106854 [Periódico revisado por pares]

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Título:
A review of applications in federated learning
Autor: Li, Li ; Fan, Yuxi ; Tse, Mike ; Lin, Kuo-Yi
Assuntos: Citation analysis ; Federated learning ; Literature review ; Research front
É parte de: Computers & industrial engineering, 2020-11, Vol.149, p.106854, Article 106854
Descrição: •Applications of New Industrial Engineering Methodologies.•Research activities relating to federated learning.•Prevailing federated learning applications.•Research fronts of federated learning. 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.
Editor: Elsevier Ltd
Idioma: Inglês

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