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

Romanini, Daniele; Hall, Adam James; Papadopoulos, Pavlos; Titcombe, Tom; Ismail, Abbas; Cebere, Tudor; Sandmann, Robert; Roehm, Robin; Hoeh, Michael A.

Authors

Daniele Romanini

Adam James Hall

Tom Titcombe

Abbas Ismail

Tudor Cebere

Robert Sandmann

Robin Roehm

Michael A. Hoeh



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.

Presentation Conference Type Poster
Conference Name ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021)
Start Date May 7, 2021
Deposit Date Oct 31, 2022
Publicly Available Date Nov 1, 2022
Public URL http://researchrepository.napier.ac.uk/Output/2946001
Publisher URL https://arxiv.org/pdf/2104.00489.pdf
Related Public URLs https://iclr.cc/virtual/2021/workshop/2148

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PyVertical: A Vertical Federated Learning Framework For Multi-headed SplitNN (990 Kb)
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