  Recently, federated learning (FL) has emerged as a promising distributed
machine learning (ML) technology, owing to the advancing computational and
sensing capacities of end-user devices, however with the increasing concerns on
users' privacy. As a special architecture in FL, vertical FL (VFL) is capable
of constructing a hyper ML model by embracing sub-models from different
clients. These sub-models are trained locally by vertically partitioned data
with distinct attributes. Therefore, the design of VFL is fundamentally
different from that of conventional FL, raising new and unique research issues.
In this paper, we aim to discuss key challenges in VFL with effective
solutions, and conduct experiments on real-life datasets to shed light on these
issues. Specifically, we first propose a general framework on VFL, and
highlight the key differences between VFL and conventional FL. Then, we discuss
research challenges rooted in VFL systems under four aspects, i.e., security
and privacy risks, expensive computation and communication costs, possible
structural damage caused by model splitting, and system heterogeneity.
Afterwards, we develop solutions to addressing the aforementioned challenges,
and conduct extensive experiments to showcase the effectiveness of our proposed
solutions.
