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.

本研究提出一种基于多方计算的安全集成联邦学习与区块链的方法，使异构模型能够在保护用户隐私的前提下协同学习医疗机构的数据，并利用区块链的属性实现数据完整性和审计控制能力。