A Federated Learning Based Privacy-Preserving Smart Healthcare System

The rapid development of the smart healthcare system makes the early-stage detection of dementia disease more user-friendly and affordable. However, the main concern is the potential serious privacy leakage of the system. In this article, we take Alzheimer's disease (AD) as an example and design a convenient and privacy-preserving system named ADDetector with the assistance of Internet of Things (IoT) devices and security mechanisms. Particularly, to achieve effective AD detection, ADDetector only collects user's audio by IoT devices widely deployed in the smart home environment and utilizes novel topic-based linguistic features to improve the detection accuracy. For the privacy breach existing in data, feature, and model levels, ADDetector achieves privacy-preserving by employing a unique three-layer (i.e., user, client, cloud, etc.) architecture. Moreover, ADDetector exploits federated learning (FL) based scheme to ensure the user owns the integrity of raw data and secure the confidentiality of the classification model and implement differential privacy (DP) mechanism to enhance the privacy level of the feature. Furthermore, to secure the model aggregation process between clients and cloud in FL-based scheme, a novel asynchronous privacy-preserving aggregation framework is designed. We evaluate ADDetector on 1010 AD detection trials from 99 health and AD users. The experimental results show that ADDetector achieves high accuracy of 81.9% and low time overhead of 0.7 s when implementing all privacy-preserving mechanisms (i.e., FL, DP, and cryptography-based aggregation).