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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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In the citing paper
“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
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
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
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Zeng, X., Yu, Z., Zhang, W., Wang, X., Lu, Q., Wang, T., Gu, M., Tian, Y., & Wang, F. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3228792 · Cited by 23
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Lydia, E. L., Anupama, C. S. S., Beno, A., Elhoseny, M., Alshehri, M. D., & Selim, M. M. (2021) · Soft Computing · doi:10.1007/s00500-021-06514-6 · Cited by 22
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Zhang, Y., Xue, X., Zhao, X., & Wang, L. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/acab22 · Cited by 22
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Jiang, S., Firouzi, F., & Chakrabarty, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299736 · Cited by 26
Du, J., Qin, N., Huang, D., Jia, X., & Zhang, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3328073 · Cited by 22
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He, N., Zhang, Z., Wang, X., & Gao, T. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/2956990 · Cited by 25
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Rana, N., & Marwaha, H. (2023) · Mathematical Foundations of Computing · doi:10.3934/mfc.2023023 · Cited by 21
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Chen, B., Zeng, X., Zhang, W., Fan, L., Cao, S., & Zhou, J. (2023) · Energy · doi:10.1016/j.energy.2023.128406 · Cited by 20
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Mondal, M. R. H., Bharati, S., Podder, P., & Kamruzzaman, J. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3030045 · Cited by 21
Alotaibi, B. K., Khan, F. A., & Mahmood, S. (2024) · Applied Sciences · doi:10.3390/app14072720 · Cited by 23
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Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 20
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Yang, Y., Liu, X., Gao, T., Xu, X., Zhang, P., & Wang, G. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3357947 · Cited by 20
Abdel‐Basset, M., Alrashdi, I., Hawash, H., Sallam, K. M., & Hameed, I. A. (2023) · Mathematics · doi:10.3390/math11143093 · Cited by 18
Krishnamoorthy, U., & Balasubramani, S. (2024) · Advanced Theory and Simulations · doi:10.1002/adts.202400479 · Cited by 18
Cheng, J., Luo, P., Xiong, N. N., & Wu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3211564 · Cited by 18
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Jiang, S., Li, Y., Firouzi, F., & Chakrabarty, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-51344-9 · Cited by 20
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Donahue, K., & Kleinberg, J. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09580 · Cited by 21
Naz, S., Phan, K. T., & Chen, Y. P. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3371222 · Cited by 17
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Gulati, S., Guleria, K., Goyal, N., AlZubi, A. A., & Castilla, Á. K. (2024) · IEEE Access · doi:10.1109/access.2024.3493946 · Cited by 21
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Iqbal, S., Zhong, X., Khan, M. A., Wu, Z., AlHammadi, D. A., Liu, W., & Choudhry, I. A. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3563909 · Cited by 17
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Majeed, A., & Zhang, X. (2023) · COVID · doi:10.3390/covid3010006 · Cited by 17
Zhu, F., Tian, Y., Han, C., Li, Y., Nan, J., Yao, N., & Zhou, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102348 · Cited by 20
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Chetoui, M., & Akhloufi, M. A. (2023) · Computers · doi:10.3390/computers12050106 · Cited by 15
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Wang, Y., Wang, F., Wang, X., Guan, Z., Ouyang, L., Wang, J., Yan, L., Zheng, W., & Zhang, W. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540207 · Cited by 14
Self-citationShi, J., Ge, B., Jiang, H., Yang, R., Si, G., Wang, Y., Gui, G., & Lin, Y. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3403229 · Cited by 14
Meerza, S. I. A., Li, Z., Liu, L., Zhang, J., & Liu, J. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871204 · Cited by 16
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Wang, X., Xie, Y., Chen, X., Yang, J., Li, R., Gao, W., Yan, Z., Zhou, H., & Ye, Z. (2026) · Journal of Medical Internet Research · doi:10.2196/79052 · Cited by 21
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Gao, Y., Gong, M., Ong, Y.-S., Qin, A. K., Wu, Y., & Xie, F. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3290188 · Cited by 13
Yu, H., Cai, L., Min, H., & Su, X. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05825-9 · Cited by 14
Hsiao, C., Lin, F. Y., Sun, T.-L., Liao, Y.-Y., Wu, C., Lai, Y.-C., Wu, H.-P., Liu, P.-R., Xiao, B.-R., Chen, C., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3400599 · Cited by 15
MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT
Ding, W., Abdel‐Basset, M., Hawash, H., & Pedrycz, W. (2022) · Information Sciences · doi:10.1016/j.ins.2022.12.017 · Cited by 13
Shen, Y., Liu, B., Yu, R., Wang, Y., Wang, S., Wu, J., & Chen, W. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995355 · Cited by 14
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Sasirekha, B., & Gunavathi, C. (2025) · Journal of Cyber Security Technology · doi:10.1080/23742917.2025.2511145 · Cited by 16
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Liang, S. Y., Nie, R., Cao, J., Wang, X., & Zhang, G. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109111 · Cited by 11
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Xu, L., Sun, H., Zhao, H., Zhang, W., Ning, H., & Guan, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3241039 · Cited by 12
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Ramesh, V., Hariharasitaraman, S., Ganesh, S. S., B., P. N., & Hemalakshmi, G. R. (2022) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-9636-4.ch019 · Cited by 11
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Nguyen, D., Imami, M. R., Zhao, L., Wu, J., Borhani, A., Mohseni, A., Khunte, M., Zhong, Z., Shi, V., Yao, S., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29819 · Cited by 13
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Shyn, S. K., Kim, D.-H., & Kim, K. (2022) · IEEE Access · doi:10.1109/access.2022.3210950 · Cited by 13
Wang, Z., Zhang, H., Wang, J., Jiang, C., He, H., & Ding, Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103881 · Cited by 11
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Firdaus, N., & Raza, Z. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8280 · Cited by 11
Dipto, S. M., Reza, M. T., Mim, N. T., Ksibi, A., Alsenan, S. A., Uddin, J., & Samad, M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-76359-0 · Cited by 16
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Chen, Y., Lin, F., Chen, Z., Tang, C., Jia, R., & Li, M. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00090 · Cited by 11
Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases
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Panda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11
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Xu, L., Chen, T., Hou, Z., Zhang, W., Hon, C., Wang, X., Wang, D., Chen, L., Zhu, W., Tian, Y., et al. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3246795 · Cited by 11
Self-citationSiddique, A. A., Talha, S. M. U., Aamir, M., Algarni, A. D., Soliman, N. F., & El‐Shafai, W. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037413 · Cited by 10
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Du, J., Qin, N., Huang, D., Jia, X., & Zhang, Y. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3546688 · Cited by 11
Zhang, A. S., & Li, N. F. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18461-1_13 · Cited by 10
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Wang, S., & Xingquan Zhu, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288157 · Cited by 9
Lai, J., Wang, T., Chen, C., Li, Y., & Zheng, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615106 · Cited by 9
Pais, V., Rao, S., Muniyal, B., & Yun, S. (2024) · Cogent Engineering · doi:10.1080/23311916.2023.2301150 · Cited by 11
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Khan, M. A., Saudagar, A. K. J., Yaqoob, M. M., Nazir, M., Yousafzai, A., Zaman, S. K. U., Alkhrijah, Y. M., & Mazhar, T. (2025) · Computing · doi:10.1007/s00607-025-01572-2 · Cited by 15
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Zhu, Z., Ai, C., Chen, H., Chen, B., Duan, W., Qiu, X., Lu, X. B., He, M., Zhao, Z., & Liu, Z. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3194639 · Cited by 8
Prity, F. S., Nath, N., Nath, A., & Uddin, K. M. A. (2023) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-023-00423-4 · Cited by 8
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Namitha, T. N., Raghavendra, S., & Rejathalal, V. (2024) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic61668.2024.10730929 · Cited by 11
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Haotian, S., Li, T., Wang, P., Xu, L., & Hongwei, Z. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/791/1/012159 · Cited by 8
FedDCS: Federated Learning Framework based on Dynamic Client Selection
Zou, S., Xiao, M., Xu, Y., An, B., & Zheng, J. (2021) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass52906.2021.00090 · Cited by 9
A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors
Mankodiya, H., Palkhiwala, P., Gupta, R., Jadav, N. K., Tanwar, S., Neagu, B.-C., Grigoraş, G., Alqahtani, F. H., & Shehata, A. M. (2022) · Mathematics · doi:10.3390/math10162927 · Cited by 9
Sahy, S. A., Mahdi, S. H., Gheni, H. M., & Barazanchi, I. I. A. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i5.4355 · Cited by 8
Zhao, R., Yang, L. T., Liu, D., Zhou, X., Deng, X., & Tang, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3273167 · Cited by 8
Islam, M. S., Hasan, K. F., Shajeeb, H. H., Rana, H. K., Rahman, M. S., Hasan, M. M., Azad, A., Abdullah, I., & Moni, M. A. (2025) · AI Open · doi:10.1016/j.aiopen.2025.01.003 · Cited by 9
Al-Hafiz, F., & Basuhail, A. A. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9010011 · Cited by 7
Kishore, J., Jain, A., Koushika, K. K., Mishra, P. K., Karanwal, S., & Solanki, S. (2025) · Discover Computing · doi:10.1007/s10791-025-09637-8 · Cited by 7
Karmode, S., & Shahade, A. K. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01089-7 · Cited by 9
FedAFR: Enhancing Federated Learning with adaptive feature reconstruction
Huang, Y., Zhu, S., Chen, W., & Huang, Z. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.12.007 · Cited by 6
An Extensive Evaluation of New Federated Learning Approaches for COVID-19 Identification
Barua, D. R., & Datta, S. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2639-8.ch014 · Cited by 6
Shi, J., Ge, B., Wu, Q., Yang, R., & Sun, Y. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02229-0 · Cited by 6
A Federated Parallel Data Platform for Trustworthy AI
Chen, L., Zhang, W., Xu, L., Zeng, X., Lu, Q., Zhao, H., Chen, B., & Wang, X. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540175 · Cited by 6
Self-citationEnergy efficient multi-tasking for edge computing using federated learning
Soni, M., Nayak, N. R., Kalra, A., Degadwala, S., Singh, N. K., & Singh, S. (2022) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-03-2022-0106 · Cited by 6
RetractedApplication of data collaboration analysis to distributed data with misaligned features
Mizoguchi, A., Imakura, A., & Sakurai, T. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101013 · Cited by 6
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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