Abstract: Alzheimer’s disease (AD) diagnosis using MRI is hindered by data-sharing restrictions. This study investigates whether federated learning (FL) can achieve high diagnostic accuracy while preserving data confidentiality. We propose an FL pipeline, utilizing EfficientNet-B3 and implemented via the Flower framework, incorporating advanced MRI segmentation (the Segment Anything Model, SAM) to isolate brain regions. The model is trained on a large ADNI MRI dataset and cross-validated on an independent OASIS dataset to evaluate generalization. Results show that our approach achieves high accuracy on ADNI (approximately 96%) and maintains strong performance on OASIS (around 85%), demonstrating robust generalization across datasets. The FL model attained high sensitivity and specificity in distinguishing AD, mild cognitive impairment, and healthy controls, validating the effectiveness of FL for AD MRI analysis. Importantly, this approach enables multi-center collaboration without sharing raw patient data. Our findings indicate that FL-trained models can be deployed across clinical sites, increasing the accessibility of advanced diagnostic tools. This work highlights the potential of FL in neuroimaging and paves the way for extension to other imaging modalities and neurodegenerative diseases.

Charmarke Moussa Abdi, Fatima-Ezzahraa Ben-Bouazza and Ali Yahyaouy, “Reinventing Alzheimer’s Disease Diagnosis: A Federated Learning Approach with Cross-Validation on Multi-Datasets via the Flower Framework” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160582

@article{Abdi2025,
title = {Reinventing Alzheimer’s Disease Diagnosis: A Federated Learning Approach with Cross-Validation on Multi-Datasets via the Flower Framework},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160582},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160582},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Charmarke Moussa Abdi and Fatima-Ezzahraa Ben-Bouazza and Ali Yahyaouy}
}