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 language | English |
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Title of host publication | EICC 2024: European Interdisciplinary Cybersecurity Conference |
Editors | Kovila Coopamootoo, Michael Sirivianos |
Publisher | ACM Press |
Pages | 186-193 |
Number of pages | 8 |
ISBN (Electronic) | 9798400716515 |
ISBN (Print) | 979-8-4007-1651-5 |
DOIs | |
Publication status | Published - 5 Jun 2024 |
Event | European Interdisciplinary Cybersecurity Conference 2024 - Democritus University of Thrace, Xanthi, Greece Duration: 5 Jun 2024 → 6 Jun 2024 https://www.fvv.um.si/eicc2024/ |
Conference
Conference | European Interdisciplinary Cybersecurity Conference 2024 |
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Abbreviated title | EICC 2024 |
Country/Territory | Greece |
City | Xanthi |
Period | 5/06/24 → 6/06/24 |
Internet address |