Federated Learning-Empowered Disease Diagnosis Mechanism in the Internet of Medical Things: From the Privacy-Preservation Perspective

Abstract

The deep integration of the Internet of Things (IoT) and the medical industry has given birth to the Internet of Medical Things (IoMT). In IoMT, physicians treat a patient's disease by analyzing patient data collected through mobile devices with the assistance of an artificial intelligence (AI)-empowered systems. However, the traditional AI technology may lead to the leakage of patient privacy data due to its own design flaws. As a privacy-preserving federated learning (FL) can generate a global disease diagnosis model through multi-party collaboration. However, FL is still unable to resist inference attacks. In this paper, to address such problems, we propose a privacy-enhanced disease diagnosis mechanism using FL for IoMT. Specifically, we first reconstruct medical data through a variational autoencoder (VAE) and add differential privacy noise to it to resist inference attacks. These data are then used to train local disease diagnosis models, thereby preserving patients' privacy. Furthermore, to encourage participation in FL, we propose an incentive mechanism to provide corresponding rewards to participants. Experiments are conducted on the arrhythmia database MIT-BIH. The experimental results show that the proposed mechanism reduces the probability of reconstructing patient medical data while ensuring high-precision heart disease diagnosis.