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.

翻译：互联网与通信技术的发展推动了工业4.0时代的到来。这一变革催生了医疗领域"医疗4.0"概念的诞生。在医疗4.0中，利用物联网医疗影像设备进行早期疾病检测，使医疗从业者能够提升医疗机构的服务质量。然而，与其他工业4.0领域相比，由于数据隐私问题，医疗4.0在人工智能与大数据应用方面仍显滞后。此外，各医疗机构存储与计算能力的差异限制了其采用统一的训练模型结构。本文提出一种基于安全多方计算的集成联邦学习方法，该方法结合区块链技术，允许异构模型在不侵犯用户隐私的前提下，协同学习医疗机构的数据。区块链特性还使参与方无需信任中央服务器即可保障数据完整性，同时为各医疗机构提供可审计性与版本控制能力。