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Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx.

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第一作者： Trung-Hieu,Hoang

第一单位： Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA.

作者： Trung-Hieu,Hoang ^[1] ; Jordan,Fuhrman ^[2] ; Marcus,Klarqvist ^[3] ; Miao,Li ^[4] ; Pranshu,Chaturvedi ^[5] ; Zilinghan,Li ^[5] ; Kibaek,Kim ^[6] ; Minseok,Ryu ^[7] ; Ryan,Chard ^[6] ; E A,Huerta ^[6] ; Maryellen,Giger ^[2] ; Ravi,Madduri ^[6]

作者单位： Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA. ^[1] University of Chicago, Chicago, IL, USA. ^[2] Broad Institute of Harvard and MIT, Cambridge, MA, USA. ^[3] Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.;School of Industrial and Systems Engineering Georgia Institute of Technology, Atlanta, GA, USA. ^[4] Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.;Department of Computer Science, University of Illinois at Urbana-Champaign, Street, Urbana, 61801, IL, USA. ^[5] Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA. ^[6] School of Computing and Augmented Intelligence Arizona State University, Tempe, AZ, USA. ^[7]

关键词 Biomedical research COVID-19 detection Chest radiographs Cloud computing Electrocardiogram Federated learning for science Function as a service Identity management Privacy-preserving

DOI 10.1016/j.csbj.2024.12.001

PMID 39896264

发布时间 2025-02-04

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准确一般字义偏差语意偏离译文作用小提交