Vertical Federated Learning: Concepts, Advances and Challenges
- URL: http://arxiv.org/abs/2211.12814v4
- Date: Thu, 28 Sep 2023 01:29:46 GMT
- Title: Vertical Federated Learning: Concepts, Advances and Challenges
- Authors: Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye,
Ye Ouyang, Ya-Qin Zhang and Qiang Yang
- Abstract summary: We review the concept and algorithms of Vertical Federated Learning (VFL)
We provide an exhaustive categorization for VFL settings and privacy-preserving protocols.
We propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints.
- Score: 18.38260017835129
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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, as well as
effectiveness and fairness constraints. Finally, we review the most recent
advances in industrial applications, highlighting open challenges and future
directions for VFL.
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