  A successful machine learning (ML) algorithm often relies on a large amount
of high-quality data to train well-performed models. Supervised learning
approaches, such as deep learning techniques, generate high-quality ML
functions for real-life applications, however with large costs and human
efforts to label training data. Recent advancements in federated learning (FL)
allow multiple data owners or organisations to collaboratively train a machine
learning model without sharing raw data. In this light, vertical FL allows
organisations to build a global model when the participating organisations have
vertically partitioned data. Further, in the vertical FL setting the
participating organisation generally requires fewer resources compared to
sharing data directly, enabling lightweight and scalable distributed training
solutions. However, privacy protection in vertical FL is challenging due to the
communication of intermediate outputs and the gradients of model update. This
invites adversary entities to infer other organisations underlying data. Thus,
in this paper, we aim to explore how to protect the privacy of individual
organisation data in a differential privacy (DP) setting. We run experiments
with different real-world datasets and DP budgets. Our experimental results
show that a trade-off point needs to be found to achieve a balance between the
vertical FL performance and privacy protection in terms of the amount of
perturbation noise.
