PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

• URL: http://arxiv.org/abs/2104.00489v1
• Date: Thu, 1 Apr 2021 14:21:33 GMT
• Title: PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
• Authors: Daniele Romanini, Adam James Hall, Pavlos Papadopoulos, Tom Titcombe, Abbas Ismail, Tudor Cebere, Robert Sandmann, Robin Roehm, Michael A. Hoeh
• Abstract summary: PyVertical is a framework supporting vertical federated learning using split neural networks. 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.
• Score: 4.552318473944852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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