A Federated Learning Based Privacy-Preserving Smart Healthcare System
Jiachun Li, Yan Meng, Lichuan Ma, Suguo Du, Haojin Zhu, Qingqi Pei, Xuemin Shen · IEEE Transactions on Industrial Informatics · 2021
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 namedADDetectorwith the assistance of Internet of Things (IoT) devices and security mechanisms. Particularly, to achieve effective AD detection,ADDetectoronly 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,ADDetectorachieves privacy-preserving by employing a unique three-layer (i.e., user, client, cloud, etc.) architecture. Moreover,ADDetectorexploitsfederated learning (FL) based schemeto ensure the user owns the integrity of raw data and secure the confidentiality of the classification model and implementdifferential privacy (DP) mechanismto enhance the privacy level of the feature. Furthermore, to secure the model aggregation process between clients and cloud in FL-based scheme, a novelasynchronous privacy-preserving aggregation frameworkis designed. We evaluateADDetectoron 1010 AD detection trials from 99 health and AD users. The experimental results show thatADDetectorachieves 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).