Toward Privacy-Preserving Federated Brain Tumor MRI Classification: A Hybrid CNN – Transformer Framework and Preliminary Evaluation

The main contributions of this study are summarized as
follows:
• Hybrid CNN–Transformer Framework: We propose
HybridNeuro, integrating ResNet50, CBAM, and Trans-
former encoder blocks for four-class brain tumor MRI
classification, with a design intended for federated de-
ployment.
• Privacy-Preserving FL Formulation: We formulate an
FL framework incorporating client-side DP-SGD, keep-
ing MRI data local and providing a basis for subsequent
privacy–utility analysis.
• Preliminary Evaluation and Failure Analysis: We
establish a centralized baseline and analyse class-wise
performance, prediction confidence, and model decision
behaviour using confusion matrices and XAI, with the
findings informing class weighting and stratified parti-
tioning for the planned FL+DP evaluation.