Version 1
: Received: 7 November 2024 / Approved: 8 November 2024 / Online: 8 November 2024 (08:47:20 CET)
How to cite:
Berkani, M. R. A.; Chouchane, A.; Belabbaci, E. O.; Ouamane, A. FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings. Preprints2024, 2024110604. https://doi.org/10.20944/preprints202411.0604.v1
Berkani, M. R. A.; Chouchane, A.; Belabbaci, E. O.; Ouamane, A. FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings. Preprints 2024, 2024110604. https://doi.org/10.20944/preprints202411.0604.v1
Berkani, M. R. A.; Chouchane, A.; Belabbaci, E. O.; Ouamane, A. FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings. Preprints2024, 2024110604. https://doi.org/10.20944/preprints202411.0604.v1
APA Style
Berkani, M. R. A., Chouchane, A., Belabbaci, E. O., & Ouamane, A. (2024). FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings. Preprints. https://doi.org/10.20944/preprints202411.0604.v1
Chicago/Turabian Style
Berkani, M. R. A., El Ouanas Belabbaci and Abdelmalik Ouamane. 2024 "FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings" Preprints. https://doi.org/10.20944/preprints202411.0604.v1
Abstract
Recent advancements in technology, particularly in artificial intelligence and privacy-preserving tools, have facilitated the development of more sophisticated and secure approaches to addressing this challenge. This paper introduces FedWell, a novel privacy-preserving Federated occupant Stress monitoring approach for smart building environments. Our system integrates physiological data from smart yoga pillows (SaYoPillow) and wearable environmental sensors to train a lightweight, edge-deployable Artificial Neural Network (ANN) model. The FedWell framework ensures data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. We conducted experiments using a comprehensive dataset of physiological parameters for stress level detection. The results demonstrate the global model’s exceptional performance, achieving 99.95% accuracy in stress level recognition, with precision of 99.95%, recall of 99.93%, and an F1-score of 99.94%. The global model exhibits a minimal loss of 0.0019% and a low communication cost of 0.08 Mb, highlighting its efficiency for real-time applications.
Keywords
Federated Learning; Deep Learning; CNN1D; FedAvg; Aggregation; Privacy-Preserving; Stress Monitoring
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
