Tree-based Models for Vertical Federated Learning: A Survey

• URL: http://arxiv.org/abs/2504.02285v1
• Date: Thu, 03 Apr 2025 05:16:09 GMT
• Title: Tree-based Models for Vertical Federated Learning: A Survey
• Authors: Bingchen Qian, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou,
• Abstract summary: Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability.<n>We conduct a series of experiments to provide empirical observations on the differences and advances of different types of tree-based models.
• Score: 71.7819045050963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability, which inspired people to apply them in vertical federated learning (VFL) scenarios in recent years. In this paper, we conduct a comprehensive study to give an overall picture of applying tree-based models in VFL, from the perspective of their communication and computation protocols. We categorize tree-based models in VFL into two types, i.e., feature-gathering models and label-scattering models, and provide a detailed discussion regarding their characteristics, advantages, privacy protection mechanisms, and applications. This study also focuses on the implementation of tree-based models in VFL, summarizing several design principles for better satisfying various requirements from both academic research and industrial deployment. We conduct a series of experiments to provide empirical observations on the differences and advances of different types of tree-based models.

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