Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
Abstract
IoT 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.
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By collectively building a global model for the prediction of brain age, the FL paradigm in the neuroimaging domain works effectively.
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From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [108]
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- Authors
- Can, Y. S., & Ersoy, C.
- Affiliations
- Koç University (TR)
- Boğaziçi University (TR)
- Venue
- ACM Transactions on Internet Technology
- Published
- 2021-01-05
- Volume
- 21
- Issue
- 1
- Pages
- 1-17
- ISSN
- 1533-5399 (ISSN-L)
- 1557-6051
- OpenAlex
- W3119097278
- Cited by
- 123
- References
- 42
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 29 · 2023: 32 · 2022: 27 · 2021: 6
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Internet Technology (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, privacy preservation, wearable devices, Internet of Things, biomedical monitoring, affective computing, mental health monitoring, stress detection, smart band, physiological signals, GDPR, California Consumer Privacy Act
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