  With the rapid development of computing technology, wearable devices such as
smart phones and wristbands make it easy to get access to people's health
information including activities, sleep, sports, etc. Smart healthcare achieves
great success by training machine learning models on a large quantity of user
data. However, there are two critical challenges. Firstly, user data often
exists in the form of isolated islands, making it difficult to perform
aggregation without compromising privacy security. Secondly, the models trained
on the cloud fail on personalization. In this paper, 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 personalized models by transfer learning. It is able
to achieve accurate and personalized healthcare without compromising privacy
and security. Experiments demonstrate that FedHealth produces higher accuracy
(5.3% improvement) for wearable activity recognition when compared to
traditional methods. FedHealth is general and extensible and has the potential
to be used in many healthcare applications.
