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.

翻译：在医疗技术飞速发展的时代，医疗数据的分割已不可避免，这促使我们需要开发能够在分布式数据上进行训练的隐私保护机器学习算法。由于《健康保险携带和责任法案》（HIPAA）施加的严格隐私法规，整合敏感医疗数据并非总是可行。本文提出了一种符合HIPAA标准的框架，该框架能够基于分布式数据进行训练。随后，我提出了一种用于阿尔茨海默病（AD）检测的多模态纵向联邦模型。阿尔茨海默病是一种严重的神经退行性疾病，可导致痴呆，严重损害大脑功能并阻碍简单任务的执行，尤其是在缺乏预防性护理的情况下。这种纵向联邦学习（VFL）模型提供了一种分布式架构，能够在尊重HIPAA隐私约束的前提下，实现跨不同医疗数据源的协同学习。本文提出的VFL架构提供了一种新颖的分布式架构，能够在遵守法定隐私约束的同时，实现跨多样化医疗数据源的协同学习。通过利用多模态数据，可以增强AD检测的鲁棒性和准确性。该模型不仅有助于推动联邦学习技术的发展，而且有望克服医学研究中由数据分割带来的障碍。