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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283
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
MIMIC-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
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
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
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
The EU General Data Protection Regulation (GDPR)
Voigt, P., & von dem Bussche, A. (2017) · doi:10.1007/978-3-319-57959-7 · Cited by 3,001
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
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484
Learning from imbalanced data: open challenges and future directions
Krawczyk, B. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0094-0 · Cited by 2,459
Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959
Challenges in Representation Learning: A Report on Three Machine Learning Contests
Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-42051-1_16 · Cited by 1,719
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · cited 1×
“[10] showed that deep learning models trained with single institutional data are vulnerable to institutional data bias, as shown in Figure 1a.”
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646
Federated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · cited 1×
“Resumes of FL applications in edge computing [29], wireless networks [30], and healthcare [31,32] also have been published.”
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection
Schmidt, P., Reiss, A., Dürichen, R., Marberger, C., & Laerhoven, K. V. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242985 · Cited by 1,508
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
A Survey on Homomorphic Encryption Schemes: Theory and Implementation
Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2019) · ACM Computing Surveys · doi:10.1145/3214303 · Cited by 1,397
Federated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005
Federated learning of predictive models from federated Electronic Health Records
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.007 · Cited by 997 · cited 6×
“…e.g., autism spectrum disorder (ASD) [18], cancer diagnosis [41,43,49], COVID ‐ 19 detection [40,44,48,54], human activity and emotion recognition [20,21,37,42,45], patient hospitalization prediction [52], patient mortality prediction [19,39,46,47,55,56], and sepsis disease diagnosis [51].”
InfluentialMulti-Classification of Brain Tumor Images Using Deep Neural Network
Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019) · IEEE Access · doi:10.1109/access.2019.2919122 · Cited by 775
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
Fredrikson, M., Lantz, E., Jha, S., Lin, S. M., Page, D., & Ristenpart, T. (2014) · PubMed · Cited by 658
Yang (2019) · doi:10.1145/3339474 · Cited by 632
Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 597
Privacy-Preserving Federated Brain Tumour Segmentation
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578
A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor
Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M. A., & Shoaib, M. (2020) · IEEE Access · doi:10.1109/access.2020.2978629 · Cited by 504
Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480
Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437
Demystifying Membership Inference Attacks in Machine Learning as a Service
Truex, S., Liu, L., Gürsoy, M. E., Yu, L., & Wei, W. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2897554 · Cited by 427
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework
Wu, Q., He, K., & Chen, X. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2993259 · Cited by 424
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392
The Non-IID Data Quagmire of Decentralized Machine LearningPreprint
Hsieh, K., Phanishayee, A., Mutlu, O., & Gibbons, P. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00189 · Cited by 353
How should systematic reviewers handle conference abstracts? A view from the trenches
Scherer, R. W., & Saldanha, I. J. (2019) · Systematic Reviews · doi:10.1186/s13643-019-1188-0 · Cited by 351
The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones
Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., & Tsiknakis, M. (2016) · ICT4AgeingWell · doi:10.5220/0005792401430151 · Cited by 316
Federated learning in a medical context: A systematic literature review
Pfitzner, B., Steckhan, N., & Arnrich, B. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3412357 · Cited by 288
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279
Federated learning for COVID-19 screening from Chest X-ray images
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107330 · Cited by 278
Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00431-6 · Cited by 278
Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · doi:10.1101/2020.08.11.20172809 · Cited by 253
Federated learning improves site performance in multicenter deep learning without data sharing
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · cited 4×
“There are several published FL studies in medical image segmentation, namely brain tumor disease [38], COVID-19 region [53], and prostate cancer region [50].”
InfluentialLi, H., Yin, Y., Fu, Z., Zhang, S., Deng, H., & Liu, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230706 · Cited by 212
Review of HIPAA, Part 1: History, Protected Health Information, and Privacy and Security Rules
Moore, W., & Frye, S. (2019) · Journal of Nuclear Medicine Technology · doi:10.2967/jnmt.119.227819 · Cited by 163
Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data
Yan, Z., Wicaksana, J., Wang, Z., Yang, X., & Cheng, K.-T. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3040015 · Cited by 137
Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
Ge, C., Gu, I. Y., Jakola, A. S., & Yang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2969805 · Cited by 127
Zhang, L., Shen, B., Barnawi, A., Xi, S., Kumar, N., & Wu, Y. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10144-6 · Cited by 112
McWilliams, A. M., Beigi, P., Srinidhi, A., Lam, S. C., & MacAulay, C. E. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2409092 · Cited by 109
Chhikara, P., Singh, P., Tekchandani, R., Kumar, N., & Guizani, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037207 · Cited by 109
COVID-19 detection using federated machine learning
Abdul Salam, M., Taha, S., & Ramadan, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0252573 · Cited by 109
Embedding AI and Crowdsourcing in the Big Data Lake
O’Leary, D. E. (2014) · IEEE Intelligent Systems · doi:10.1109/mis.2014.82 · Cited by 102
MICE vs PPCA: Missing data imputation in healthcare
Hegde, H., Shimpi, N. A., Panny, A., Glurich, I., Christie, P., & Acharya, A. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100275 · Cited by 100
Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images
Feng, Y., Zhang, L., & Mo, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2858763 · Cited by 89
Xue, Z., Zhou, P., Xu, Z., Wang, X., Xie, Y., Ding, X., & Wen, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3057653 · Cited by 89
Lee, H., Chai, Y. J., Joo, H., Lee, K., Hwang, J. Y., Kim, S., Kim, K., Nam, I., Choi, J. Y., Yu, H. W., et al. (2021) · JMIR Medical Informatics · doi:10.2196/25869 · Cited by 78
Cloud-based federated learning implementation across medical centers
Rajendran, S., Obeid, J. S., Binol, H., D`Agostino, R., Foley, K., Zhang, W., Austin, P., Brakefield, J., Gurcan, M. N., & Topaloglu, U. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00060 · Cited by 76
Liu, J., Goetz, J., Sen, S., & Tewari, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/23728 · Cited by 61
Xue, W., Li, Q., & Xue, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2961964 · Cited by 60
Deep Learning for Electronic Health Records Analytics
Harerimana, G., Kim, J. W., Yoo, H., & Jang, B. (2019) · IEEE Access · doi:10.1109/access.2019.2928363 · Cited by 56
Putra, K. T., Chen, H., Prayitno, P., Ogiela, M. R., Chou, C., Weng, C., & Shae, Z. (2021) · Sensors · doi:10.3390/s21134586 · Cited by 53
Self-citationChen, S., Yang, H., Fu, J., Mei, W.-J., Ren, S., Liu, Y., Zhu, Z., Liu, L., Li, H., & Chen, H. (2019) · IEEE Access · doi:10.1109/access.2019.2923760 · Cited by 49
Self-citationFeARH: Federated machine learning with anonymous random hybridization on electronic medical records
Cui, J., Zhu, H. G., Deng, H., Chen, Z., & Liu, D. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103735 · Cited by 48
Cha, D., Sung, M., & Park, Y. R. (2021) · JMIR Medical Informatics · doi:10.2196/26598 · Cited by 47
SPIE-AAPM PROSTATEx Challenge Data
Litjens, G., Debats, O. A., Barentsz, J. O., Karssemeijer, N., & Huisman, H. (2017) · The Cancer Imaging Archive · doi:10.7937/k9tcia.2017.murs5cl · Cited by 47
Alserr, N. A., Ayday, E., & Ulusoy, Ö. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa475 · Cited by 28
Artificial intelligence for medical imaging
Tran, K., Boetker, J. P., Aframian, A., & Memarzadeh, K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818438-7.00006-x · Cited by 24
Shao, R., He, H., Chen, Z., Liu, H., & Liu, D. (2020) · JMIR Formative Research · doi:10.2196/17265 · Cited by 13
Data breach at major healthcare firms
(2019) · Computer Fraud & Security · doi:10.1016/s1361-3723(19)30059-4 · Cited by 6
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.
- Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated learning with non-IID data. arXiv.
- Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., and Cummings, R. (2019). Advances and open problems in federated learning. arXiv.
- Caldas, S., Meher Karthik Duddu, S., Wu, P., Li, T., Konečný, J., McMahan, H.B., Smith, V., and Talwalkar, A. (2018). LEAF: A benchmark for federated settings. arXiv.
- Luo, J., Wu, X., Luo, Y., Huang, A., Huang, Y., Liu, Y., and Yang, Q. (2021). Real-world image datasets for federated learning. arXiv.
- Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 image data collection. arXiv.
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
Teo, 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
Self-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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
