A Federated Learning-Based Light-Weight Privacy-Preserving Framework for Smart Healthcare Systems

Abstract

Smart healthcare systems have been widely applied in the fields of intelligent healthcare, self-monitoring, diagnosis, and emergency. In recent years, there have been growing concerns regarding the privacy of the data collected from the users of the smart healthcare systems. This chapter proposes a light-weight federated learning framework based on multi-key homomorphic encryption for deploying predictive models trained on patient data distributed across multiple healthcare institutions without exchanging them. Two predictive models based on the proposed framework are deployed for in-house mortality prediction from patient data and COVID-19 detection from chest x-ray images. Performance evaluation of these models with standard datasets and comparative analyses show that the proposed models are superior to state-of-the-art approaches. The proposed framework and the models are potential solutions to improve the quality of healthcare across multiple healthcare institutions, protecting the sensitive patient data and ensuring personalization of healthcare.