Paper Info
Title
Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
Abstract
AbstractIoT devices generate massive amounts of biomedical data with increased digitalization and development of the state-of-the-art automated clinical data collection systems. When combined with advanced machine learning algorithms, the big data could be useful to improve the health systems for decision-making, diagnosis, and treatment. Mental healthcare is also attracting attention, since most medical problems can be associated with mental states. Affective computing is among the emerging biomedical informatics fields for automatically monitoring a person’s mental state in ambulatory environments by using physiological and physical signals. However, although affective computing applications are promising to improve our daily lives, before analyzing physiological signals, privacy issues and concerns need to be dealt with. Federated learning is a promising candidate for developing high-performance models while preserving the privacy of individuals. It is a privacy protection solution that stores model parameters instead of the data itself and abides by the data protection laws such as EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). We applied federated learning to heart activity data collected with smart bands for stress-level monitoring in different events. We achieved encouraging results for using federated learning in IoT-based wearable biomedical monitoring systems by preserving the privacy of the data.
Year
DOI
Venue
2021
10.1145/3428152
ACM Transactions on Internet Technology
Keywords
DocType
Volume
Privacy-preserving, deep learning, stress detection, affective computing, smartwatch, PPG, federated learning, data protection
Journal
21
Issue
ISSN
Citations 
1
1533-5399
2
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Can, Yekta Said1122.38
Cem Ersoy2135791.36