FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

TL;DR

FedHealth introduces a federated transfer learning framework that enables privacy-preserving, personalized healthcare models for wearable devices, significantly improving activity recognition accuracy.

Contribution

It is the first framework combining federated learning and transfer learning specifically for wearable healthcare applications.

Findings

Achieves 5.3% higher accuracy in activity recognition.

Preserves user privacy while enabling model personalization.

Demonstrates extensibility to various healthcare tasks.

Abstract

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.