Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain

被引：12
|
作者：
Stephanie, Veronika [1 ]
Khalil, Ibrahim [1 ]
Atiquzzaman, Mohammed [2 ]
Yi, Xun [1 ]
机构：
[1] RMIT Univ, Sch Comp Technol, Melbourne, Vic 3000, Australia
[2] Univ Oklahoma, Sch Comp Sci, Norman, OK 73019 USA
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2023年 / 19卷 / 07期
基金：
澳大利亚研究理事会;
关键词：
Artificial intelligent (AI); blockchain; deep learning (DL); ensemble learning; federated learning (FL); privacy preservation; secure multiparty computation; FRAMEWORK;
D O I：
10.1109/TII.2022.3214998
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of Things-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in artificial intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This article presents a secure multiparty computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.
引用
收藏
页码：7936 / 7945
页数：10
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