Tree-based Models for Vertical Federated Learning: A Survey
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915225198919680 |
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| author | Qian, Bingchen Xie, Yuexiang Li, Yaliang Ding, Bolin Zhou, Jingren |
| author_facet | Qian, Bingchen Xie, Yuexiang Li, Yaliang Ding, Bolin Zhou, Jingren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tree-based Models for Vertical Federated Learning: A Survey Qian, Bingchen Xie, Yuexiang Li, Yaliang Ding, Bolin Zhou, Jingren Machine Learning Artificial Intelligence 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. |
| title | Tree-based Models for Vertical Federated Learning: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.02285 |