research-article

Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring

Authors:

Yekta Said Can,

Cem ErsoyAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 1

Article No.: 21, Pages 1 - 17

https://doi.org/10.1145/3428152

Published: 05 January 2021 Publication History

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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Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 1

Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things

February 2021

534 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3441681

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2021 ACM.

© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 05 January 2021

Accepted: 01 October 2020

Revised: 01 September 2020

Received: 01 June 2020

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Cited By

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https://doi.org/10.4258/hir.2024.30.1.3
V. SR. J(2024)Applications of Machine Learning Algorithms in Data Encryption StandardsMachine Learning and Cryptographic Solutions for Data Protection and Network Security10.4018/979-8-3693-4159-9.ch008(111-134)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-4159-9.ch008
Vashishth TSharma VSharma KKumar BChaudhary SGupta M(2024)Privacy-Preserving Machine Learning Techniques for IoT Data in Cloud EnvironmentsEmerging Technologies for Securing the Cloud and IoT10.4018/979-8-3693-0766-3.ch006(144-173)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-0766-3.ch006
Ferrara E(2024)Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and ChallengesSensors10.3390/s2415504524:15(5045)Online publication date: 4-Aug-2024
https://doi.org/10.3390/s24155045
Huang YTeng TLi YZhang M(2024)Privacy information protection of medical internet based on double chaos encryption algorithmJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23767046:4(7779-7789)Online publication date: 18-Apr-2024
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Barnawi AChhikara PTekchandani RKumar NAlzahrani B(2024)A Differentially Privacy Assisted Federated Learning Scheme to Preserve Data Privacy for IoMT ApplicationsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.339396921:4(4686-4700)Online publication date: 1-Aug-2024
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Cortiñas-Lorenzo KLacey G(2024)Toward Explainable Affective Computing: A ReviewIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327002735:10(13101-13121)Online publication date: Oct-2024
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Zhu JShi YFu MZhou YWu YFu L(2024)Latency Minimization for Wireless Federated Learning With Heterogeneous Local Model UpdatesIEEE Internet of Things Journal10.1109/JIOT.2023.328593711:1(444-461)Online publication date: 1-Jan-2024
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Vyas ALin PHwang RTripathi M(2024)Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A SurveyIEEE Access10.1109/ACCESS.2024.345421112(127018-127050)Online publication date: 2024
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