eprintid: 9229 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/92/29 datestamp: 2023-10-17 23:30:13 lastmod: 2023-10-17 23:30:15 status_changed: 2023-10-17 23:30:13 type: article metadata_visibility: show creators_name: Alvi, Sohaib Bin Khalid creators_name: Nayyer, Muhammad Ziad creators_name: Jamal, Muhammad Hasan creators_name: Raza, Imran creators_name: de la Torre Diez, Isabel creators_name: Rodríguez Velasco, Carmen Lilí creators_name: Breñosa, Jose creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: carmen.rodriguez@uneatlantico.es creators_id: josemanuel.brenosa@uneatlantico.es creators_id: title: A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Public health, federated learning, edge computing, deep learning 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. date: 2023-10 publication: DIGITAL HEALTH volume: 9 id_number: doi:10.1177/20552076231203604 refereed: TRUE issn: 2055-2076 official_url: http://doi.org/10.1177/20552076231203604 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Alvi, Sohaib Bin Khalid; Nayyer, Muhammad Ziad; Jamal, Muhammad Hasan; Raza, Imran; de la Torre Diez, Isabel; Rodríguez Velasco, Carmen Lilí; Breñosa, Jose y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carmen.rodriguez@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2023) A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation. DIGITAL HEALTH, 9. ISSN 2055-2076 document_url: http://repositorio.unic.co.ao/id/eprint/9229/1/alvi-et-al-2023-a-lightweight-deep-learning-approach-for-covid-19-detection-using-x-ray-images-with-edge-federation.pdf