Vertical Federated Learning (VFL) is a privacy-preserving distributed
learning paradigm where different parties collaboratively learn models using
partitioned features of shared samples, without leaking private data. Recent
research has shown promising results addressing various challenges in VFL,
highlighting its potential for practical applications in cross-domain
collaboration. However, the corresponding research is scattered and lacks
organization. To advance VFL research, this survey offers a systematic overview
of recent developments. First, we provide a history and background
introduction, along with a summary of the general training protocol of VFL. We
then revisit the taxonomy in recent reviews and analyze limitations in-depth.
For a comprehensive and structured discussion, we synthesize recent research
from three fundamental perspectives: effectiveness, security, and
applicability. Finally, we discuss several critical future research directions
in VFL, which will facilitate the developments in this field. We provide a
collection of research lists and periodically update them at
this https URL

纵向联邦学习是一种隐私保护的分布式学习范式，不泄露私密数据的情况下，不同参与方通过使用共享样本的分区特征协同学习模型。本文对纵向联邦学习的最新发展进行了系统概述，并提出了几个关键的未来研究方向，以促进该领域的发展。