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

Federated learning promises to make machine learning feasible on distributed,
private datasets by implementing gradient descent using secure aggregation
methods. The idea is to compute a global weight update without revealing the
contributions of individual users. Current practical protocols for secure
aggregation work in an "honest but curious" setting where a curious adversary
observing all communication to and from the server cannot learn any private
information assuming the server is honest and follows the protocol. A more
scalable and robust primitive for privacy-preserving protocols is shuffling of
user data, so as to hide the origin of each data item. Highly scalable and
secure protocols for shuffling, so-called mixnets, have been proposed as a
primitive for privacy-preserving analytics in the Encode-Shuffle-Analyze
framework by Bittau et al., which was later analytically studied by Erlingsson
et al. and Cheu et al.. The recent papers by Cheu et al., and Balle et al. have
given protocols for secure aggregation that achieve differential privacy
guarantees in this "shuffled model". Their protocols come at a cost, though:
Either the expected aggregation error or the amount of communication per user
scales as a polynomial $n^{\Omega(1)}$ in the number of users $n$. In this
paper we propose simple and more efficient protocol for aggregation in the
shuffled model, where communication as well as error increases only
polylogarithmically in $n$. Our new technique is a conceptual "invisibility
cloak" that makes users' data almost indistinguishable from random noise while
introducing zero distortion on the sum.

该论文提出了一个在混淆模型下更高效的隐私安全的聚合协议，可以使得通信量和误差只呈对数级增长，并且采用了一个称为 ' 隐身斗篷 ' 的新技术来实现此目的，该技术可以使得每个数据项与噪声几乎不可区分，同时对总和不会造成任何扭曲。