Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Namitha, T.N.; Raghavendra, S.; Vinith, R. Title A name given to the resource Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Online and Biomedical Engineering;Volume;22;Issue;2;pp.109-123 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3991/ijoe.v22i02.58737" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v22i02.58737</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030255633?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030255633?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource adaptive model selection; ADASYN; convolutional neural network (CNN); federated learning (FL); flower framework; intravenous immunoglobulin (IVIG) resistance; Kawasaki disease (KD) Publisher An entity responsible for making the resource available International Federation of Engineering Education Societies (IFEES) Relation A related resource ISSN: 26268493; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online