  In the era of rapidly advancing medical technologies, the segmentation of
medical data has become inevitable, necessitating the development of privacy
preserving machine learning algorithms that can train on distributed data.
Consolidating sensitive medical data is not always an option particularly due
to the stringent privacy regulations imposed by the Health Insurance
Portability and Accountability Act (HIPAA). In this paper, we introduce a HIPAA
compliant framework that can train from distributed data. We then propose a
multimodal vertical federated model for Alzheimer's Disease (AD) detection, a
serious neurodegenerative condition that can cause dementia, severely impairing
brain function and hindering simple tasks, especially without preventative
care. This vertical federated model offers a distributed architecture that
enables collaborative learning across diverse sources of medical data while
respecting privacy constraints imposed by HIPAA. It is also able to leverage
multiple modalities of data, enhancing the robustness and accuracy of AD
detection. Our proposed model not only contributes to the advancement of
federated learning techniques but also holds promise for overcoming the hurdles
posed by data segmentation in medical research. By using vertical federated
learning, this research strives to provide a framework that enables healthcare
institutions to harness the collective intelligence embedded in their
distributed datasets without compromising patient privacy.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing