# A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2312.10237 · HTML: https://mlsanity.com/paper/2312.10237/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Transfer Learning for EEG Signal Classification](https://mlsanity.com/paper/2004.12321/) (2020)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://mlsanity.com/paper/2112.10166/) (2021)
- [HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography](https://mlsanity.com/paper/1909.05784/) (2019)
- [A Vertical Federated Learning Framework for Horizontally Partitioned Labels](https://mlsanity.com/paper/2106.10056/) (2021)
- [Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning](https://mlsanity.com/paper/2108.06958/) (2021)

## Built on by (most cited first, 1 of 1)

- [HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification](https://mlsanity.com/paper/2512.10701/) (2025)

## Abstract (arXiv)

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, I introduce a HIPAA compliant framework that can train from distributed data. I 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 learning (VFL) model offers a distributed architecture that enables collaborative learning across diverse sources of medical data while respecting privacy constraints imposed by HIPAA. The VFL architecture proposed herein offers a novel distributed architecture, enabling collaborative learning across diverse sources of medical data while respecting statutory privacy constraints. By leveraging multiple modalities of data, the robustness and accuracy of AD detection can be enhanced. This 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.