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JACIII Vol.29 No.4 pp. 857-867
doi: 10.20965/jaciii.2025.p0857
(2025)

Research Paper:

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Asynchronous and Flexible Federated Learning for COVID-19 Detection from Chest X-Ray Images

Mohamed Hedi Elhajjej, Salwa Said ORCID Icon, Nouha Arfaoui ORCID Icon, and Ridha Ejbali ORCID Icon

Research Team in Intelligent Machines (RTIM), National School of Engineers of Gabes, University of Gabes
Omar Ibn El Khattab, Zrig Eddakhlania, Gabes 6072, Tunisia

Received:
November 5, 2024
Accepted:
April 8, 2025
Published:
July 20, 2025
Creative Commons License
Keywords:
chest X-rays, federated learning, COVID-19 screening, heterogeneous models, privacy-preserving AI
Abstract

Machine learning, particularly deep learning, is a powerful tool for assisting radiologists in analyzing large volumes of chest X-ray images, significantly accelerating disease diagnosis. However, privacy regulations and data ownership challenges often hinder the centralization of sensitive patient data required for training. Federated learning (FL) addresses these issues by enabling decentralized model training while preserving data confidentiality. This paper introduces a novel FL framework for secure COVID-19 screening using chest X-rays. Our approach incorporates asynchronous communication to overcome delays caused by device heterogeneity and minimizes server-client interactions to reduce network traffic, enhancing scalability. Furthermore, the framework supports heterogeneous client models, ensuring optimized local training. These innovations preserve privacy while achieving performance levels comparable to centralized systems, setting a benchmark for privacy-preserving AI in healthcare.

Cite this article as:
M. Elhajjej, S. Said, N. Arfaoui, and R. Ejbali, “Asynchronous and Flexible Federated Learning for COVID-19 Detection from Chest X-Ray Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.4, pp. 857-867, 2025.
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Last updated on Jul. 19, 2025