  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.
