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Comparative Analysis between Individual, Centralized, and Federated Learning for Smartwatch Based Stress Detection.

Muhammad Ali Fauzi1, Bian Yang1, Bernd Blobel2,3,4

  • 1Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway.

Journal of Personalized Medicine
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

Individual learning achieved the highest accuracy for stress detection using smartwatch data. Federated learning, while preserving privacy, did not match the performance of centralized or individual approaches in this study.

Keywords:
centralized learningfederated learningindividual learningmachine learningprivacysmartwatchstress detection

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Area of Science:

  • Wearable technology
  • Machine learning applications
  • Biomedical signal processing

Background:

  • Machine learning models for stress detection require substantial data, often necessitating centralized data collection.
  • Centralized data aggregation raises significant user privacy concerns due to the sensitive nature of personal health information.
  • Federated learning offers a decentralized approach to train models without compromising user data privacy.

Purpose of the Study:

  • To implement and evaluate a federated learning approach for stress detection using smartwatch sensor data.
  • To compare the performance of federated learning against individual and centralized learning strategies for stress detection.
  • To analyze the trade-offs between privacy preservation and model accuracy in stress detection.

Main Methods:

  • The study utilized the WESAD dataset, which includes multi-modal sensor data from smartwatches.
  • Logistic Regression was employed as the classification algorithm for stress detection.
  • A comparative analysis was performed between individual learning, centralized learning, and federated learning paradigms.

Main Results:

  • Individual learning demonstrated superior performance, achieving an average accuracy of 0.9998 and an F1-measure of 0.9996.
  • Federated learning, while enabling decentralized training, did not attain the accuracy levels of individual or centralized learning.
  • The results indicate a performance gap between federated learning and traditional centralized/individual methods for this specific stress detection task.

Conclusions:

  • Individual learning provides the highest accuracy for stress detection with the WESAD dataset.
  • Federated learning presents a privacy-preserving alternative but currently underperforms compared to centralized and individual methods in terms of accuracy.
  • Further research is needed to optimize federated learning algorithms for improved performance in wearable-based health monitoring.