ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1633916
This article is part of the Research TopicOptimizing Health Outcomes through XAI and Digital Twins in Media InterventionsView all articles
FLEM-XAI: Federated Learning based real time Ensemble Model with explainable AI framework for an efficient diagnosis of Lung Diseases
Provisionally accepted- 1TIFAC CORE in Cyber Security, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham., Coimbatore, India
- 2Karunya Institute of Technology and Sciences, Coimbatore, India
- 3CMR University, Bengaluru, India
- 4Sri Krishna College of Engineering and Technology, Coimbatore, India
- 5Department of Information, Communication and Electronics Engineering, Catholic University of Korea,, Korea, Republic of Korea
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The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learningbased model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5%, 8%, 9%, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8%, 88%, 92.5%, and 95.5%, respectively.
Keywords: Central Server based Learning Model, Federated learning, Ensemble model, Explainable AI (XAI), SHAP (SHapley Additive exPlanations), Grad-CAM, DP, Lung Diseases
Received: 23 May 2025; Accepted: 09 Jul 2025.
Copyright: © 2025 S, Daniel, S, V K and Sachnev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Durga S, TIFAC CORE in Cyber Security, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham., Coimbatore, India
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