GTV: Generating Tabular Data via Vertical Federated Learning

Synthetic data has emerged as a promising avenue for privacy-preserving data sharing. However, constructing synthetic data generators necessitates access to the real dataset, posing challenges, particularly when data features are disparately distributed across different organizations. Vertical Federated Learning (VFL) is a collaborative approach to training machine learning models among distinct tabular data holders, such as financial institutions, who possess disjoint features for the same group of customers. In this paper, we introduce the GTV framework for Generating Tabular Data via Vertical Federated Learning and demonstrate that VFL can be successfully used to implement GANs for distributed tabular data in a privacy-preserving manner, with performance close to centralized GANs which assume shared data. We make design choices with respect to the distribution of GAN generator and discriminator models, and we introduce a training-with-shuffling technique so that no party can reconstruct training data from the GAN conditional vector. The paper presents (1) an implementation of GTV, (2) a detailed quality evaluation of GTV-generated synthetic data, (3) an examination of the GTV framework for different data distributions and number of clients, and (4) an analysis of GTV's robustness against Membership Inference Attacks with different settings of Differential Privacy, for a range of datasets with diverse distribution characteristics. Our results demonstrate that GTV can consistently generate high-fidelity synthetic tabular data of comparable quality to that generated by a centralized GAN algorithm. The difference in machine learning utility can be as low as 2.7%, even under extremely imbalanced data distributions across clients. Code is available at: https://github.com/zhao-zilong/gtv