  Federated Learning (FL) has emerged as a promising distributed learning
paradigm with an added advantage of data privacy. With the growing interest in
having collaboration among data owners, FL has gained significant attention of
organizations. The idea of FL is to enable collaborating participants train
machine learning (ML) models on decentralized data without breaching privacy.
In simpler words, federated learning is the approach of ``bringing the model to
the data, instead of bringing the data to the mode''. Federated learning, when
applied to data which is partitioned vertically across participants, is able to
build a complete ML model by combining local models trained only using the data
with distinct features at the local sites. This architecture of FL is referred
to as vertical federated learning (VFL), which differs from the conventional FL
on horizontally partitioned data. As VFL is different from conventional FL, it
comes with its own issues and challenges. In this paper, we present a
structured literature review discussing the state-of-the-art approaches in VFL.
Additionally, the literature review highlights the existing solutions to
challenges in VFL and provides potential research directions in this domain.
