TY  - JOUR
JF  - DIGITAL HEALTH
N2  - Objective
This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy.
Method
The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture.
Results
The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture.
Conclusion
Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance.
Y1  - 2023/10//
A1  - Alvi, Sohaib Bin Khalid
A1  - Nayyer, Muhammad Ziad
A1  - Jamal, Muhammad Hasan
A1  - Raza, Imran
A1  - de la Torre Diez, Isabel
A1  - Rodríguez Velasco, Carmen Lilí
A1  - Breñosa, Jose
A1  - Ashraf, Imran
SN  - 2055-2076
KW  - Public health
KW  -  federated learning
KW  -  edge computing
KW  -  deep learning
UR  - http://doi.org/10.1177/20552076231203604
AV  - public
ID  - uninimx9229
VL  - 9
TI  - A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation
ER  -