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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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · cited 9×
“This label distribution skew characteristic was initially demonstrated in FedAvg’s experiment [16].”
InfluentialMIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · cited 2×
“Medical Information Mart for Intensive Care (MIMIC) III [76] The MIMIC III dataset was collected from 40,000 patients during stayed in the ICU at Beth Israel Deaconess Medical Center between 2001 and 2012.”
Calibrating Noise to Sensitivity in Private Data Analysis
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)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · cited 1×
“Brain Tumor Image Segmentation Benchmark (BraTS) 2017 and 2018 [71] The BraTs 2017 were collected from 13 institutions and consisted of 359 patients’ brain tumor scans.”
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
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“, xn) and some sample instances, the membership inference attack task tries to discover whether the instance exists or not in the training dataset [61].”
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“Good performance of the deep learning model on smart healthcare applications highly depends on a diverse and vast amount of training data [9].”
Research works printed but not linked: 6
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Cited by
Sorted by citations
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Privacy preservation for federated learning in health care
Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J. J., Lakshminarayanan, A., Wang, S.-H., Sheller, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.100974 · Cited by 294
“25,42 This potentially enables collaborations to be formed with larger andmorediversetrainingdata,whichcanresultintrainedmodels that generalize better.”
BackgroundTeo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228
Putra, K. T., Arrayyan, A. Z., Hayati, N. Q., Firdaus, F., Damarjati, C., Bakar, A., & Chen, H. (2024) · IEEE Access · doi:10.1109/access.2024.3358827 · Cited by 164
“Such data anomalies can be adeptly mitigated and refined through the deployment of Deep Learning (DL) [15] and EFL paradigms [16].”
BackgroundSelf-citation
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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