ABSTRACT
This chapter highlights the difficulties encountered during the model's training as well as the proposed approach for the model that has been used to address the issue of Alzheimer's disease detection systems. Federated learning is a model that has lately gained popularity because it shows great potential for learning from private information that is dispersed. The suggested technique is thoroughly examined using metrics like precision, recall, and accuracy on the Alzheimer's Dataset, which comprises 6400 MRI pictures. The system for detecting Alzheimer's disease is concluded in the fourth section, along with plans for improvements. The pictures from each class are as follows: The dataset was thoroughly examined using several different channels, and it was discovered that neither multiple images from the same patient nor repeated MRI scans of the patients were present in the dataset.
