We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device. To link entities shared across different datasets' partitions, we use Private Set Intersection on IDs associated with data points. To demonstrate the validity of the proposed framework, we present the training of a simple dual-headed split neural network for a MNIST classification task, with data samples vertically distributed across two data owners and a data scientist.
翻译:我们引入了支持利用分裂神经网络进行纵向联合学习的框架PyVertic。拟议框架允许数据科学家对神经网络进行关于数据特征的培训,数据特征在多个所有者之间垂直分割,同时保留所有者设备上的原始数据。为了将不同数据集分区共享的实体联系起来,我们在与数据点相关的身份识别上使用私自设置交叉路段。为了证明拟议框架的有效性,我们介绍了为MNIST分类任务培训一个简单的双头分裂神经网络,数据样本垂直分布在两个数据所有者和一位数据科学家之间。