FedRadar: Federated Multi-Task Transfer Learning for Radar-Based Internet of Medical Things

Abstract

Owing to increasing connected medical devices and rapid development of deep learning technologies, the Internet of Medical Things (IoMT) has attracted great attention for healthcare monitoring with intelligent sensors. Radar serves as a non-contact healthcare device, continuously measuring human’s vital signs and behavior to provide daily and comprehensive long-term record on health status. However, radar sensor data collected from various users and families usually involve sensitive personal information, while traditional deep learning technologies using IoMT radar data may present high privacy leakage risks. This paper proposes FedRadar, a novel federated multi-task transfer learning framework for radar-based heartbeat rate and activity monitoring in IoMT to solve these challenges. It deals with the decentralized structure, training personalized multi-task models collaboratively, combining the shared relevance knowledge of human’s physiological information while keeping personal radar data locally for privacy protection. First, the multi-task neural network is built on the spatial-temporal radar data to capture the potential relationship and shared representations between human’s vital sign and activity. Furthermore, the federated learning with knowledge transfer scheme is designed to achieve personalized local models by transferring coarse relevant features and keeping fine-grained individual information. Extensive experiments demonstrate the effectiveness and robustness of FedRadar with 2.8% and 2.5% superior than local training model respectively on the accuracy of heartbeat rate estimation and activity classification in realistic constructed radar datasets. In addition, FedRadar is extensible and suitable to continuously monitor multiple health indicators with privacy protection in IoMT. The FedRadar codes and constructed radar datasets are available on https://github.com/bupt-uwb/FedRadar.