Research Area:  Machine Learning
With the rapid development of computing technology, wearable devices make it easy to get access to peoples health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning. Wearable activity recognition experiments and real Parkinsons disease auxiliary diagnosis application have evaluated that FedHealth is able to achieve accurate and personalized healthcare without compromising privacy and security. FedHealth is general and extensible in many healthcare applications.
Author(s) Name:  Yiqiang Chen; Xin Qin; Jindong Wang; Chaohui Yu; Wen Gao
Journal name:  Intelligent Systems
Publisher name:  IEEE
Volume Information:  Volume: 35, Issue: 4, Page(s): 83 - 93
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9076082