AlzFed-XAI: High-Fidelity Interpretable Alzheimer's Diagnosis with Privacy-Preserving Federated Learning

Data privacy constraints hinder deep learning in medical imaging by preventing data centralization. We introduce AlzFed-XAI, a federated learning framework for Alzheimer's diagnosis from decentralized MRIs. AlzFed-XAI trains a lightweight CNN (FedNet, 378K parameters) across data silos without exposing raw patient information. On the imbalanced OASIS-1 dataset, our framework achieves 99.73\% accuracy and a 0.9970 macro F1-score, demonstrating a negligible performance drop compared to a centralized baseline. To foster clinical trust, Grad-CAM visualizations confirm the model learns neuroanatomically relevant features. Our work presents a robust, privacy-by-design solution, demonstrating a viable pathway for building high-performance, interpretable AI for critical healthcare diagnostics.