Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
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DOI：
10.48550/arxiv.2308.11027
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Zhuohang Li;Chao Yan;Xinmeng Zhang;Gharib Gharibi;Zhijun Yin;Xiaoqian Jiang;B. Malin
中科院分区：
文献类型：
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作者：
Zhuohang Li;Chao Yan;Xinmeng Zhang;Gharib Gharibi;Zhijun Yin;Xiaoqian Jiang;B. Malin
深度学习继续快速发展，现在在许多医学预测任务中显示出巨大的潜力。然而，实现在医疗保健组织中推广的深度学习模型具有挑战性。这部分是由于这些组织固有的孤立性质和患者隐私要求。为了解决这个问题，我们说明了分裂学习如何在不同的和私人维护的健康数据集上实现深度学习模型的协作训练，同时保持原始记录和模型参数的私密性。我们引入了一个新的隐私保护分布式学习框架，与传统的联邦学习相比，它提供了更高级别的隐私。我们使用几个生物医学成像和电子健康记录（EHR）数据集来证明，通过分裂学习训练的深度学习模型可以实现与集中式和联合式模型高度相似的性能，同时大大提高计算效率并降低隐私风险。
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.