  Federated learning is a popular collaborative learning approach that enables
clients to train a global model without sharing their local data. Vertical
federated learning (VFL) deals with scenarios in which the data on clients have
different feature spaces but share some overlapping samples. Existing VFL
approaches suffer from high communication costs and cannot deal efficiently
with limited overlapping samples commonly seen in the real world. We propose a
practical vertical federated learning (VFL) framework called \textbf{one-shot
VFL} that can solve the communication bottleneck and the problem of limited
overlapping samples simultaneously based on semi-supervised learning. We also
propose \textbf{few-shot VFL} to improve the accuracy further with just one
more communication round between the server and the clients. In our proposed
framework, the clients only need to communicate with the server once or only a
few times. We evaluate the proposed VFL framework on both image and tabular
datasets. Our methods can improve the accuracy by more than 46.5\% and reduce
the communication cost by more than 330$\times$ compared with state-of-the-art
VFL methods when evaluated on CIFAR-10. Our code will be made publicly
available at \url{https://nvidia.github.io/NVFlare/research/one-shot-vfl}.
