Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

翻译：深度学习持续快速发展，目前已在众多医学预测任务中展现出显著潜力。然而，实现能在医疗机构间泛化的深度学习模型仍面临挑战，这在一定程度上源于医疗机构固有的数据孤岛特性及患者隐私保护需求。针对该问题，我们阐释了分割学习如何能够在保持原始记录和模型参数私密性的前提下，实现跨异构且私密维护的健康数据集进行深度学习模型的协作训练。我们提出了一种新型隐私保护分布式学习框架，该框架相较于传统联邦学习具有更高的隐私保护水平。通过使用多个生物医学成像与电子健康记录数据集，我们证明经由分割学习训练的深度学习模型在获得与集中式及联邦式模型高度相似性能的同时，还能够显著提升计算效率并降低隐私风险。