A Federated Multi-Model DL Framework for Early Alzheimer’s Disease Prediction with Preserving and Explainability Features

This research proposes a Federated Multi-Modal Deep Learning Framework (FedMM-AD) with explainability features and privacy-preserving techniques for early-stage AD prediction. Our federated model was constructed using MRI, PET, and CT scan data, protecting patient privacy and security while enabling cross-institution training of the model without sharing any patient information. The FedMM-AD model’s accuracy, AUC, sensitivity, and specificity were 98.59%, 97.3%, and 98.2%, respectively, according to the experiment conducted on the ADNI and OASIS datasets. In order to increase the model’s transparency and help doctors identify key brain regions for AD, we integrated explainable AI techniques employing cross-attention. The findings show that when additional stakeholders, like clinicians, are properly involved, the approach can be beneficial and have an impact.