The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Open accessAbstract
Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.
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“Similarly, Zhang et al. [24] and Yan et al. [29] used X-ray and CT image data for different Convolutional Neural Network (CNN) architectures in FL settings.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“Moreover, Zhang et al. [24] proposed a FL oriented COVID-19 detection model where chest X-ray and CT images were considered from three datasets, Qatar-Dhaka data, COVID-CT, and Figure 1 dataset.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“Next, within the 10,000 sample range, seven papers [21], [24], [25], [27], [28], [31] and [32] used data samples between 2,109 and 6,284 and this number is quite good.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“Only two articles we defined as VFL; [24] have taken three datasets, two X-ray and one CT image based.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“[24] have taken three datasets, two X-ray and one CT image based.In the article the authors combined the both types of images and used them to train and test models.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“Score of [30], [25] and [24] is not clear, they discussed the accuracy between 90-95%.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“A dynamic fusion based vertical FL pro-”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
In the citing paper
“Non-IID data is not considered.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [24]
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Cites
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
Advances and Open Problems in Federated Learning
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Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734
Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355
Federated Learning for Mobile Keyboard Prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084
Federated Learning of Deep Networks using Model AveragingPreprint
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005
Blockchained On-Device Federated Learning
Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887
Asynchronous Federated OptimizationPreprint
Xie, C., Koyejo, O., & Gupta, I. (2019) · arXiv.org · doi:10.48550/arxiv.1903.03934 · Cited by 828
Cited by
Sorted by citations
Federated learning for smart healthcare: A survey
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827
Federated learning for healthcare: Systematic review and architecture proposal
Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501813 · Cited by 595
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518
Model aggregation techniques in federated learning: A comprehensive survey
Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.008 · Cited by 496
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366
Metadata
- Authors
- Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y.
- Affiliations
- China University of Petroleum, East China (CN)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Chinese Academy of Sciences (CN)
- Southern University of Science and Technology (CN)
- Corresponding author
- Zhang, Weishan
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-11-01
- Volume
- 8
- Issue
- 21
- Pages
- 15884-15891
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- PMC
- PMC9128757
- OpenAlex
- W3127299377
- Cited by
- 329
- References
- 33
- Citations by year
- 2026: 25 · 2025: 49 · 2024: 67 · 2023: 86 · 2022: 63 · 2021: 36
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62072469)
- National Key Research and Development Program (2018YFE0116700, 2020YFB2104301)
- Shandong Provincial Natural Science Foundation (ZR2019MF049)
- Fundamental Research Funds for the Central Universities (2015020031)
- Project “PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications” (LZC0019)
- Special Project of West Coast Artificial Intelligence Technology Innovation Center (2019-1-5, 2019-1-6)
- Opening Project of Shanghai Trusted Industrial Control Platform (TICPSH202003015-ZC)
- Project “Beihang Beidou Technological Achievements Transformation and Industrialization Funds” (BARI2005)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, dynamic fusion, COVID-19 detection, computed tomography, X-ray images, communication efficiency, data heterogeneity, client selection, privacy-preserving machine learning, fault tolerance, medical image dataset
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