ItemArticle

Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease

Title
Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease
Creator
Namitha, T.N.; Raghavendra, S.; Vinith, R.
Description
Kawasaki disease (KD), a rare pediatric illness affecting children under five, is treated with intravenous immunoglobulin (IVIG). But 1020% of patients are resistant to IVIG, and these resistant kids face a higher risk of coronary artery abnormalities. Identifying resistance early is vital, yet data scarcity, class imbalance, and the diseases rarity necessitate nationwide collaboration, which is often hindered by country-specific privacy policies. Federated learning (FL) provides a practical way for different parties to collaborate on training a model while keeping their raw data private and secure. To enhance model adaptability across diverse clinical populations, we propose an adaptive intermediate model selection strategy in federated learning. Each client retains the versionglobal or locally fine-tunedthat performs best on its own data, using customizable performance metrics such as F1-score or recall. The system was implemented using the Flower FL framework, with three simulated clients and a shared convolutional neural network (CNN) architecture. Experiments demonstrated that the global model achieved stronger performance than conventional models, and several clients obtained further gains by selecting intermediate models aligned with their data. This approach introduces a novel balance between worldwide collaboration and local personalization in FL, offering a flexible and clinically meaningful solution for IVIG resistance prediction. 2026 by the authors of this article.
Source
International Journal of Online and Biomedical Engineering;Volume;22;Issue;2;pp.109-123
Date
01-01-2026
Publisher
International Federation of Engineering Education Societies (IFEES)
Subject
adaptive model selection; ADASYN; convolutional neural network (CNN); federated learning (FL); flower framework; intravenous immunoglobulin (IVIG) resistance; Kawasaki disease (KD)
Coverage
Namitha T.N., Christ (Deemed to be University), Bangalore, India; Raghavendra S., Christ (Deemed to be University), Bangalore, India; Vinith R., Amrita Vishwa Vidyapeetham, Coimbatore, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 26268493;
Format
online
Language
English
Type
Article

Citation

Namitha, T.N.; Raghavendra, S.; Vinith, R., “Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23598.

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