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

Jeroen Klein Brinke, Martijn van der Linden, Paul J.M. Havinga

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

42 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationEICC 2024: European Interdisciplinary Cybersecurity Conference
EditorsKovila Coopamootoo, Michael Sirivianos
PublisherACM Press
Pages186-193
Number of pages8
ISBN (Electronic)9798400716515
ISBN (Print)979-8-4007-1651-5
DOIs
Publication statusPublished - 5 Jun 2024
EventEuropean Interdisciplinary Cybersecurity Conference 2024 - Democritus University of Thrace, Xanthi, Greece
Duration: 5 Jun 20246 Jun 2024
https://www.fvv.um.si/eicc2024/

Conference

ConferenceEuropean Interdisciplinary Cybersecurity Conference 2024
Abbreviated titleEICC 2024
Country/TerritoryGreece
CityXanthi
Period5/06/246/06/24
Internet address

Fingerprint

Dive into the research topics of 'Using Federated Learning and Channel State Information-Based Sensing for Scalable and Realistic At-Home Healthcare'. Together they form a unique fingerprint.

Cite this