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Review

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

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Department of Computer Science and Information Engineering, Asia University, Taichung City 413, Taiwan
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Department of Electrical Engineering, Politeknik Negeri Semarang, Semarang 50275, Indonesia
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Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
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Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
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Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 404, Taiwan
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Department of M-Commerce and Multimedia Applications, Asia University, Taichung City 413, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Stefano Silvestri and Francesco Gargiulo
Appl. Sci. 2021, 11(23), 11191; https://doi.org/10.3390/app112311191
Received: 12 November 2021 / Revised: 22 November 2021 / Accepted: 23 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Big Data for eHealth Applications)
Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications. View Full-Text
Keywords: federated learning; deep learning; artificial intelligence; healthcare; data privacy-preserving federated learning; deep learning; artificial intelligence; healthcare; data privacy-preserving
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MDPI and ACS Style

Prayitno; Shyu, C.-R.; Putra, K.T.; Chen, H.-C.; Tsai, Y.-Y.; Hossain, K.S.M.T.; Jiang, W.; Shae, Z.-Y. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Appl. Sci. 2021, 11, 11191. https://doi.org/10.3390/app112311191

AMA Style

Prayitno, Shyu C-R, Putra KT, Chen H-C, Tsai Y-Y, Hossain KSMT, Jiang W, Shae Z-Y. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Applied Sciences. 2021; 11(23):11191. https://doi.org/10.3390/app112311191

Chicago/Turabian Style

Prayitno, Chi-Ren Shyu, Karisma Trinanda Putra, Hsing-Chung Chen, Yuan-Yu Tsai, K. S. M. Tozammel Hossain, Wei Jiang, and Zon-Yin Shae. 2021. "A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications" Applied Sciences 11, no. 23: 11191. https://doi.org/10.3390/app112311191

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