  Autoencoders are popular neural networks that are able to compress high
dimensional data to extract relevant latent information. TabNet is a
state-of-the-art neural network model designed for tabular data that utilizes
an autoencoder architecture for training. Vertical Federated Learning (VFL) is
an emerging distributed machine learning paradigm that allows multiple parties
to train a model collaboratively on vertically partitioned data while
maintaining data privacy. The existing design of training autoencoders in VFL
is to train a separate autoencoder in each participant and aggregate the latent
representation later. This design could potentially break important
correlations between feature data of participating parties, as each autoencoder
is trained on locally available features while disregarding the features of
others. In addition, traditional autoencoders are not specifically designed for
tabular data, which is ubiquitous in VFL settings. Moreover, the impact of
client failures during training on the model robustness is under-researched in
the VFL scene. In this paper, we propose TabVFL, a distributed framework
designed to improve latent representation learning using the joint features of
participants. The framework (i) preserves privacy by mitigating potential data
leakage with the addition of a fully-connected layer, (ii) conserves feature
correlations by learning one latent representation vector, and (iii) provides
enhanced robustness against client failures during training phase. Extensive
experiments on five classification datasets show that TabVFL can outperform the
prior work design, with 26.12% of improvement on f1-score.
