A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications
Open accessAbstract
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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Reviews [3], [9], and [6] included the whole healthcare area to survey and review papers.
“Similarly in [3], [9], and [6] the authors have included the whole healthcare area to survey and review the papers.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [6]
The review followed PRISMA procedures.
“To conduct this review we followed the PRISMA procedures which is the most common way of performing SLR in the healthcare sector [6].”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [6]
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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
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-11-25
- Volume
- 11
- Issue
- 23
- Pages
- 11191
- ISSN
- 2076-3417 (ISSN-L)
- 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
- 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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