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

文献类型：期刊论文

作者Zhang, Weishan2; Zhou, Tao2; Lu, Qinghua3,4; Wang, Xiao5; Zhu, Chunsheng1,6; Sun, Haoyun2; Wang, Zhipeng2; Lo, Sin Kit3,4; Wang, Fei-Yue5
刊名IEEE INTERNET OF THINGS JOURNAL
出版日期2021-11-01
卷号8期号:21页码:15884-15891
ISSN号2327-4662
关键词Data models COVID-19 Training Collaborative work Analytical models Servers Predictive models AI COVID-19 CT federated learning image processing machine learning X-Ray
DOI10.1109/JIOT.2021.3056185
通讯作者Zhang, Weishan(zhangws@upc.edu.cn) ; Lu, Qinghua(qinghua.lu@data61.csiro.au) ; Zhu, Chunsheng(chunsheng.tom.zhu@gmail.com)
英文摘要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.
资助项目National Natural Science Foundation of China[62072469] ; National Key Research and Development Program[2018YFE0116700] ; National Key Research and Development Program[2020YFB2104301] ; Shandong Provincial Natural Science Foundation (Parallel Data-Driven Fault Prediction Under Online-Offline Combined Cloud Computing Environment)[ZR2019MF049] ; Fundamental Research Funds for the Central Universities[2015020031] ; Special Project of West Coast Artificial Intelligence Technology Innovation Center[2019-1-5] ; Special Project of West Coast Artificial Intelligence Technology Innovation Center[2019-1-6] ; Opening Project of Shanghai Trusted Industrial Control Platform[TICPSH202003015-ZC] ; Project Beihang Beidou Technological Achievements Transformation and Industrialization Funds[BARI2005] ; PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications[LZC0019]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000711808500026
资助机构National Natural Science Foundation of China ; National Key Research and Development Program ; Shandong Provincial Natural Science Foundation (Parallel Data-Driven Fault Prediction Under Online-Offline Combined Cloud Computing Environment) ; Fundamental Research Funds for the Central Universities ; Special Project of West Coast Artificial Intelligence Technology Innovation Center ; Opening Project of Shanghai Trusted Industrial Control Platform ; Project Beihang Beidou Technological Achievements Transformation and Industrialization Funds ; PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications
源URL[http://ir.ia.ac.cn/handle/173211/46779]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Zhang, Weishan; Lu, Qinghua; Zhu, Chunsheng
作者单位1.PCL Res Ctr Networks & Commun, Peng Cheng Lab, Shenzhen 518066, Peoples R China
2.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
3.CSIRO, Data61, Sydney, NSW 2015, Australia
4.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Southern Univ Sci & Technol, Inst Future Networks, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Weishan,Zhou, Tao,Lu, Qinghua,et al. Dynamic-Fusion-Based Federated Learning for COVID-19 Detection[J]. IEEE INTERNET OF THINGS JOURNAL,2021,8(21):15884-15891.
APA Zhang, Weishan.,Zhou, Tao.,Lu, Qinghua.,Wang, Xiao.,Zhu, Chunsheng.,...&Wang, Fei-Yue.(2021).Dynamic-Fusion-Based Federated Learning for COVID-19 Detection.IEEE INTERNET OF THINGS JOURNAL,8(21),15884-15891.
MLA Zhang, Weishan,et al."Dynamic-Fusion-Based Federated Learning for COVID-19 Detection".IEEE INTERNET OF THINGS JOURNAL 8.21(2021):15884-15891.

入库方式： OAI收割

来源：自动化研究所

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