论文标题
分裂驱动的垂直分区
SplitNN-driven Vertical Partitioning
论文作者
论文摘要
在这项工作中，我们介绍了由SplitNN驱动的垂直分区，这是一种称为SplitNN的分布式深度学习方法的配置，以促进从垂直分布的特征中学习。 SplitNN不会与协作机构共享原始数据或模型详细信息。所提出的配置允许在拥有各种数据源的机构之间进行培训，而无需复杂的加密算法或安全的计算协议。我们评估了几种配置以合并拆分模型的输出，并比较性能和资源效率。该方法是灵活的，并且允许许多不同的配置应对垂直分裂数据集提出的特定挑战。
In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features. SplitNN does not share raw data or model details with collaborating institutions. The proposed configuration allows training among institutions holding diverse sources of data without the need of complex encryption algorithms or secure computation protocols. We evaluate several configurations to merge the outputs of the split models, and compare performance and resource efficiency. The method is flexible and allows many different configurations to tackle the specific challenges posed by vertically split datasets.
