Cited by 329Privacy-Preserving Technologies in DataCOVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

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Dynamic-Fusion-Based Federated Learning for COVID-19 Detection

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Zhang, Weishan, Zhou, Tao, Lu, Qinghua, Wang, Xiao, Zhu, Chunsheng, Sun, Haoyun, Wang, Zhipeng, Lo, Sin Kit, Wang, Fei-Yue·2021·IEEE Internet of Things Journal

DOI 10.1109/jiot.2021.3056185PMC PMC9128757

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Abstract

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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Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875
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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
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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
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Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084
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Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887
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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: Zhang, Weishan
Zhou, Tao
Lu, Qinghua
Wang, Xiao
Zhu, Chunsheng
Sun, Haoyun
Wang, Zhipeng
Lo, Sin Kit
Wang, Fei-Yue
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-11-01
Volume: 8
Issue: 21
Pages: 15884-15891
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2021.3056185
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-009 (stm-asf)
doi.org/10.15223/policy-001 (stm-asf)
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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