ArticleJournal of personalized medicine2022
Comparative Analysis between Individual, Centralized, and Federated Learning for Smartwatch Based Stress Detection.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Passive AI Detection of Stress and Burnout Among Frontline Workers.Nursing reports (Pavia, Italy) · 2025Review
- A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.Scientific reports · 2025Article
- Standardization and Interoperability: Federated Learning's Impact on EHR Systems and Health Informatics.Advances in health information science and practice · 2025Article
- Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.JMIR mental health · 2024Article
- Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- Smartwatches in healthcare medicine: assistance and monitoring; a scoping review.BMC medical informatics and decision making · 2023Article
- Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model.Sensors (Basel, Switzerland) · 2023Article
- Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been proven to provide good performances on stress detection tasks using multi-modal sensor data from a smartwatch. Generally, machine learning techniques need a sufficient amount of data to train a robust model. Thus, we need to collect data from several users and send them to a central server to feed the algorithm. However, the uploaded data may contain sensitive information that can jeopardize the user's privacy. Federated learning can tackle this challenge by enabling the model to be trained using data from all users without the user's data leaving the user's device. In this study, we implement federated learning-based stress detection and provide a comparative analysis between individual, centralized, and federated learning. The experiment was conducted on WESAD dataset by using Logistic Regression as the classifier. The experiment results show that in terms of accuracy, federated learning cannot reach the performance level of both individual and centralized learning. The individual learning strategy performs best with an average accuracy of 0.9998 and an average F
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.