Using Federated Learning and Channel State Information-Based Sensing for Scalable and Realistic At-Home Healthcare

This paper explores the use of federated learning in a realistic household employing existing infrastructure to add new devices and locations by rotating the role of the transmitter among smart devices in a multi-person scenario. Current solutions employ channel state information-based sensing for health care monitoring in various ways to propagate knowledge efficiently; however, these solutions often consider (i) ideally placed devices in (ii) single-participant scenarios and (iii) do not consider the different roles of these devices in a network. Data is collected from four smart devices in a household, assuming three participants, one of which is monitored and the other two function as noise, are assigned to perform activities to replicate a realistic household scenario. Insights are provided on using federated learning in realistic at-home health care when adding a new activity location and client devices, both transmitter-only and full communication devices. Results indicate new devices and locations can quickly be adopted with less data by the federated model without intensive retraining, even in multi-person environments, when doing extensive pre-training.