Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches the description of samples using features from different parties to improve model capacity. Compared with horizontal federated learning, in most cases, VFL is applied in the commercial cooperation scenario of companies. Therefore, VFL contains tremendous business values. In the past few years, VFL has attracted more and more attention in both academia and industry. In this paper, we systematically investigate the current work of VFL from a layered perspective. From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL. Moreover, the application of VFL has covered a wide range of areas, e.g., finance, healthcare, etc. At each layer, we categorize the existing work and explore the challenges for the convenience of further research and development of VFL. Especially, we design a novel MOSP tree taxonomy to analyze the core component of VFL, i.e., secure vertical federated machine learning algorithm. Our taxonomy considers four dimensions, i.e., machine learning model (M), protection object (O), security model (S), and privacy-preserving protocol (P), and provides a comprehensive investigation.
翻译:纵向联邦学习(Vertical federated learning,VFL)是面向数据垂直分区和分布在各方之间的联邦学习的一种有前途的范畴。 VFL使用来自不同方的特征来丰富样本的描述,从而提高模型容量。与水平联邦学习相比,在大多数情况下,VFL应用于公司之间的商业合作场景。因此,VFL具有巨大的商业价值。在过去的几年中,VFL在学术界和工业界越来越受到关注。本文从分层视角系统地调查了VFL的当前工作。从硬件层到纵向联邦系统层,研究人员为VFL的各个方面做出了贡献。此外,VFL的应用领域涵盖了广泛的领域,例如金融、医疗保健等。在每个层面上,我们对现有工作进行分类,并探讨了进一步研究和开发VFL的挑战。特别地,我们设计了一种新的MOSP树分类法,以分析VFL的核心组件,即安全的纵向联邦机器学习算法。我们的分类考虑了机器学习模型(Model)、保护对象(Object)、安全模型(Security)和隐私保护协议(Protocol)四个维度,提供了全面的调查。