Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain

The advancement of Internet and Communication Technologies (ICTs) 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 IoT-enabled medical imaging devices for early disease detection has enabled medical pr...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-07, Vol.19 (7), p.1-10
Main Authors: Stephanie, Veronika, Khalil, Ibrahim, Atiquzzaman, Mohammed, Yi, Xun
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial Intelligent
Big Data
Blockchain
Blockchains
Computational modeling
Cryptography
Data models
Data privacy
Deep Learning
Ensemble Learning
Federated Learning
Health care
Hospitals
Industry 4.0
Internet of Things
Medical electronics
Medical imaging
Privacy
Privacy Preservation
Secure Multi-party Computation
Servers
Version control
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Summary:The advancement of Internet and Communication Technologies (ICTs) 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 IoT-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 paper presents a secure multi-party 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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3214998