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.

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，这促使我们开发能够在分布式数据上训练且保护隐私的机器学习算法。由于《健康保险可携性及责任法案》（HIPAA）实施的严格隐私法规，整合敏感医疗数据并非始终可行。本文提出一种符合HIPAA要求的框架，可基于分布式数据进行训练。随后，我们提出一种多模态纵向联邦模型，用于检测阿尔茨海默病（AD）——一种可能导致痴呆的严重神经退行性疾病，会严重损害大脑功能并阻碍基本任务执行，尤其在缺乏预防性护理时更为显著。该纵向联邦模型采用分布式架构，能够在遵守HIPAA隐私约束的前提下，实现跨多种医疗数据源的协作学习，同时利用多模态数据增强阿尔茨海默病检测的鲁棒性与准确性。所提模型不仅推动了联邦学习技术的发展，更有望克服医学研究中数据分割带来的挑战。通过采用纵向联邦学习，本研究旨在提供一个框架，使医疗机构能够在保护患者隐私的同时，充分利用分布式数据集中蕴含的集体智慧。