  Generative Adversarial Networks (GANs) have achieved state-of-the-art results
in tabular data synthesis, under the presumption of direct accessible training
data. Vertical Federated Learning (VFL) is a paradigm which allows to
distributedly train machine learning model with clients possessing unique
features pertaining to the same individuals, where the tabular data learning is
the primary use case. However, it is unknown if tabular GANs can be learned in
VFL. Demand for secure data transfer among clients and GAN during training and
data synthesizing poses extra challenge. Conditional vector for tabular GANs is
a valuable tool to control specific features of generated data. But it contains
sensitive information from real data - risking privacy guarantees. In this
paper, we propose GTV, a VFL framework for tabular GANs, whose key components
are generator, discriminator and the conditional vector. GTV proposes an unique
distributed training architecture for generator and discriminator to access
training data in a privacy-preserving manner. To accommodate conditional vector
into training without privacy leakage, GTV designs a mechanism
training-with-shuffling to ensure that no party can reconstruct training data
with conditional vector. We evaluate the effectiveness of GTV in terms of
synthetic data quality, and overall training scalability. Results show that GTV
can consistently generate high-fidelity synthetic tabular data of comparable
quality to that generated by centralized GAN algorithm. The difference on
machine learning utility can be as low as to 2.7%, even under extremely
imbalanced data distributions across clients and different number of clients.
