Version 1
: Received: 1 November 2024 / Approved: 4 November 2024 / Online: 4 November 2024 (14:29:28 CET)
How to cite:
Aymene Berkani, M. R.; Chouchane, A.; Belabbaci, E. O. Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach. Preprints2024, 2024110179. https://doi.org/10.20944/preprints202411.0179.v1
Aymene Berkani, M. R.; Chouchane, A.; Belabbaci, E. O. Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach. Preprints 2024, 2024110179. https://doi.org/10.20944/preprints202411.0179.v1
Aymene Berkani, M. R.; Chouchane, A.; Belabbaci, E. O. Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach. Preprints2024, 2024110179. https://doi.org/10.20944/preprints202411.0179.v1
APA Style
Aymene Berkani, M. R., Chouchane, A., & Belabbaci, E. O. (2024). Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach. Preprints. https://doi.org/10.20944/preprints202411.0179.v1
Chicago/Turabian Style
Aymene Berkani, M. R., Ammar Chouchane and El Ouanas Belabbaci. 2024 "Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach" Preprints. https://doi.org/10.20944/preprints202411.0179.v1
Abstract
This paper presents a novel privacy-preserving approach, Fed-CNN1D (Federated Convolutional Neural Network 1D), based on federated learning for monitoring air quality and classifying different activities of daily living in indoor spaces. The system employs six different types of sensors to collect measurement parameters, which are then used to train a 1D CNN model locally for activity recognition. The proposed model is lightweight and edge deployable, making it suitable for real-time applications. Experiments were conducted using an air quality dataset specifically curated for Activity of Daily Living (ADL) classification. The results demonstrate that our approach Fed-CNN1D achieves 96.50% accuracy, 96.27% F1-Score, 96.54% precision, 96.21% recall, 0.11% loss, with low communication cost of 0.099Mb, and a swift detection time of 15 ms.
Keywords
ADL; air quality; federated learning; deep learning; CNN; FedAvg
Subject
Computer Science and Mathematics, Signal Processing
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.
