The DOI matches its Crossref record; 70 of the 71 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
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MIMIC-III, a freely accessible critical care database
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Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
The multimodal brain tumor image segmentation benchmark (BRATS)
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Federated Machine Learning: Concept and Applications
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Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
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Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
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Flagged first, then by citations
1 of the 71 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D. (2019). Federated learning for mobile keyboard prediction. arXiv.
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Cited by
Sorted by citations
Items 201–209 of 209
Federated Learning Framework for Privacy-Preserving Healthcare Diagnosis
Manjusha, B., Manivrath, G., Deepthi, D., Tripathy, H. K., Tharangini, M. P., & Jyothi, P. (2026) · International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11577067
Effects of Data Heterogeneity and Differential Privacy on Federated Learning
Meßmer, L.-M., Reich, C., Ziekow, H., & Abdeslam, D. O. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24804-6_30
Optical Character Recognition Dengan Metode Artificial Neural Network HammingPreprint
Yunistyawan, F., Suwarno, I., Raharja, N. M., Alkarim, M. H., Ma’arif, A., & Widyasmoro, W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7232985
Wearable neural biosensors for early detection of neurodegenerative diseases
Ahmad, I., Ravindra, K., Bhargavi, Y. B., Nagaraju, K. L., Mandava, J., Mim, T. J., & Dongare, S. A. (2026) · Discover Neuroscience · doi:10.1186/s13064-026-00302-4
Federated Learning in Smart Healthcare, Homes, and Cities (FL-SHHC): A Systematic Literature Review
Jackson, T., Khan, J. A., & Mylonas, A. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7090485
Zuhri, H. S., Purwanto, Y., & Ruriawan, M. F. (2026) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom70739.2026.11714370
Priesa, Z. C., Purwanto, Y., & Ruriawan, M. F. (2026) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom70739.2026.11714358
MMT: Achieving Exact Federated Unlearning with Improved Post-unlearning Performance
Zhang, Z., Bui, N., Verma, A., Ding, B., & Low, B. K. H. (2026) · Machine Learning · doi:10.1007/s10994-026-07117-8
Equity-Aware Agentic AI Architectures and Governance With SDG-Aligned Healthcare Innovation
Parihar, D., JAIN, S. H., Sharma, A., & Sharma, D. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9705-4.ch014
Metadata
- 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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