Cited by 210Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

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

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Prayitno, Shyu, Chi-Ren, Putra, Karisma Trinanda, Chen, Hsing-Chung, Tsai, Yuan-Yu, Hossain, K. S. M. Tozammel, Jiang, Wei, Shae, Zon-Yin·2021·Applied Sciences

DOI 10.3390/app112311191

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Abstract

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.

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Authors: Prayitno, Shyu, C.-R., Putra, K. T., Chen, H.-C., Tsai, Y.-Y., Hossain, K. S. M. T., Jiang, W., & Shae, Z.-Y.
Affiliations: Department of Computer Science and Information Engineering, Asia University, Taichung City 413, Taiwan
University of Missouri (US)
Asia University (TW)
Corresponding author: Shae, Zon-Yin
ORCID: Prayitno
Shyu, Chi-Ren
Putra, Karisma Trinanda
Chen, Hsing-Chung
Tsai, Yuan-Yu
Hossain, K. S. M. Tozammel
Jiang, Wei
Shae, Zon-Yin
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2021-11-25
Volume: 11
Issue: 23
Pages: 11191
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app112311191
OpenAlex: W3215776046
Cited by: 210
References: 71
Citations by year: 2026: 37 · 2025: 47 · 2024: 46 · 2023: 58 · 2022: 22
FWCI: 2.27
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Applied Sciences (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Ministry of Science and Technology (MOST 110-2321-B-468-001, MOST 110-2511-H-468-005)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: federated learning, health care, medical data privacy, data partitioning, data distribution skew, data protection mechanisms, benchmark dataset, smart healthcare

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

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