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 https://github.com/shentt67/VFL_Survey.

翻译：纵向联邦学习（Vertical Federated Learning, VFL）是一种隐私保护的分布式学习范式，参与方利用共享样本的划分特征协同训练模型，且不泄露私有数据。近期研究在应对VFL中的各类挑战方面已展现出有前景的成果，凸显了其在跨领域协作中实际应用的潜力。然而，相关研究较为分散且缺乏系统性梳理。为推进VFL研究，本文对近期进展进行了系统性综述。首先，我们回顾了VFL的历史与背景，并概述了其通用训练流程。随后，我们重新审视了近期综述中的分类体系，并深入分析了现有局限。为展开全面而有条理的讨论，我们从三个基本维度——效能、安全性与适用性——对近期研究进行了综合梳理。最后，我们探讨了VFL未来若干关键研究方向，以促进该领域的发展。我们在https://github.com/shentt67/VFL_Survey 提供了相关研究文献列表并定期更新。