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

Abstract

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.

References in corpus (4)

• MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
• One weird trick for parallelizing convolutional neural networks
• A Secure Federated Learning Framework for 5G Networks
• Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things

Cited by in corpus (1)

• FLoW3 -- Web3 Empowered Federated Learning