Vertical Federated Learning (VFL) is a federated learning setting where
multiple parties with different features about the same set of users jointly
train machine learning models without exposing their raw data or model
parameters. Motivated by the rapid growth in VFL research and real-world
applications, we provide a comprehensive review of the concept and algorithms
of VFL, as well as current advances and challenges in various aspects,
including effectiveness, efficiency, and privacy. We provide an exhaustive
categorization for VFL settings and privacy-preserving protocols and
comprehensively analyze the privacy attacks and defense strategies for each
protocol. In the end, we propose a unified framework, termed VFLow, which
considers the VFL problem under communication, computation, privacy, and
effectiveness constraints. Finally, we review the most recent advances in
industrial applications, highlighting open challenges and future directions for
VFL.

本文提供了关于垂直联邦学习的综述，包括联邦学习的概念、算法及其各个方面的挑战和现有进展；介绍了 VFL 的分类、隐私保护协议、攻击和防御策略，并提出了 VFLow 统一框架；最后还回顾了工业应用的最新进展和存在的开放性问题和未来发展方向。

垂直联邦学习

Vertical Federated Learning

Generative flows are promising tractable models for density modeling that
define probabilistic distributions with invertible transformations. However,
tractability imposes architectural constraints on generative flows, making them
less expressive than other types of generative models. In this work, we study a
previously overlooked constraint that all the intermediate representations must
have the same dimensionality with the original data due to invertibility,
limiting the width of the network. We tackle this constraint by augmenting the
data with some extra dimensions and jointly learning a generative flow for
augmented data as well as the distribution of augmented dimensions under a
variational inference framework. Our approach, VFlow, is a generalization of
generative flows and therefore always performs better. Combining with existing
generative flows, VFlow achieves a new state-of-the-art 2.98 bits per dimension
on the CIFAR-10 dataset and is more compact than previous models to reach
similar modeling quality.

本文介绍了一种通过增加数据而扩展中间维度限制的方法 VFlow，提高了生成流模型的表达能力，并在 CIFAR-10 数据集上实现 2.98 bits per dimension 的最新成果。