  Federated learning allows multiple parties to build machine learning models
collaboratively without exposing data. In particular, vertical federated
learning (VFL) enables participating parties to build a joint machine learning
model based upon distributed features of aligned samples. However, VFL requires
all parties to share a sufficient amount of aligned samples. In reality, the
set of aligned samples may be small, leaving the majority of the non-aligned
data unused. In this article, we propose Federated Cross-view Training
(FedCVT), a semi-supervised learning approach that improves the performance of
the VFL model with limited aligned samples. More specifically, FedCVT estimates
representations for missing features, predicts pseudo-labels for unlabeled
samples to expand the training set, and trains three classifiers jointly based
upon different views of the expanded training set to improve the VFL model's
performance. FedCVT does not require parties to share their original data and
model parameters, thus preserving data privacy. We conduct experiments on
NUS-WIDE, Vehicle, and CIFAR10 datasets. The experimental results demonstrate
that FedCVT significantly outperforms vanilla VFL that only utilizes aligned
samples. Finally, we perform ablation studies to investigate the contribution
of each component of FedCVT to the performance of FedCVT.

Machine Learning;Machine Learning