@article{uninimx9229,
            year = {2023},
         journal = {DIGITAL HEALTH},
          volume = {9},
          author = {Sohaib Bin Khalid Alvi and Muhammad Ziad Nayyer and Muhammad Hasan Jamal and Imran Raza and Isabel de la Torre Diez and Carmen Lil{\'i} Rodr{\'i}guez Velasco and Jose Bre{\~n}osa and Imran Ashraf},
           month = {Octubre},
           title = {A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation},
             url = {http://repositorio.unini.edu.mx/id/eprint/9229/},
        abstract = {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.},
        keywords = {Public health, federated learning, edge computing, deep learning}
}