Year: 2026
Type: article
Abstract: Magnetic resonance imaging (MRI) is widely used for brain‐tumor assessment, but conventional deep learning workflows often require patient images to be centralized, creating privacy, governance, and regulatory barriers. This study presents a lightweight federated learning (FL) framework for brain MRI classification using a multilayer perceptron (MLP) trained with the Flower framework and Federated Averaging (FedAvg). In the revised formulation, the task is treated strictly as classification: each MRI‐derived sample is represented by a 239‐dimensional feature vector and assigned to one of the encoded diagnostic classes in the label column. Segmentation terminology has been removed or clarified, and the Dice coefficient is reported only as a class‐level F1/Dice‐style classification metric rather than as a pixel‐level tumor‐overlap score. The prepared BraTS‐derived subset used for simulation contains 3000 .npy samples organized for training, normal/abnormal representation, and mask‐associated metadata; the full BraTS source and the preprocessed subset are now distinguished to avoid confusion. The FL setting is explicitly described as a simulation in which a single prepared dataset is partitioned into artificial clients, not as a true multihospital deployment. The proposed MLP contains 39,814 trainable parameters, requiring approximately 0.152 MB per model update in a 32‐bit floating‐point format, which makes it substantially cheaper to communicate than typical CNN‐based alternatives. The best reported FL–MLP configuration achieved 95% accuracy, 0.92 class‐level Dice/F1, 0.91 sensitivity, and 0.97 specificity. The revised manuscript also clarifies that FL reduces the need for raw data sharing but does not provide formal privacy guarantees unless combined with secure aggregation, differential privacy, encryption, or attack‐resistance mechanisms.
DOI: 10.1155/acis/2118911
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
OpenAlex ID: W7202091348