4,340 research outputs found
Vertical Federated Learning
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
Vertical Federated Learning:A Structured Literature Review
Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain
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