Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly

• URL: http://arxiv.org/abs/2502.08160v1
• Date: Wed, 12 Feb 2025 07:03:32 GMT
• Title: Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
• Authors: Zhaomin Wu, Zhen Qin, Junyi Hou, Haodong Zhao, Qinbin Li, Bingsheng He, Lixin Fan,
• Abstract summary: This survey analyzes the real-world data distributions in potential Vertical Federated Learning (VFL) applications. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.
• Score: 42.31182713177944
• License:
• Abstract: Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL systems in real-world applications remains limited. To investigate the gap between existing VFL research and practical deployment, this survey analyzes the real-world data distributions in potential VFL applications and identifies four key findings that highlight this gap. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Our comprehensive review of existing VFL algorithms reveals that some common practical VFL scenarios have few or no viable solutions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.

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