在本次工作中，我们介绍了分裂网络驱动的垂直分区，这是一种分布式深度学习方法的配置，称为SplitN，以方便从垂直分布特征中学习。SplitNN不与合作机构共享原始数据或模型细节。拟议的配置允许在持有不同数据源的机构之间进行瞄准，而不需要复杂的加密算法或安全计算协议。我们评估几种配置来合并分割模型的输出，并比较性能和资源效率。该方法非常灵活，允许使用多种不同的配置来解决垂直拆分数据集所带来的具体问题。
原文题目：SplitNN-driven Vertical Partitioning
原文：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.
原文作者:Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar
原文地址：https://arxiv.org/abs/2008.04137
SplitNN-driven垂直分区(CS).pdf