Tree-based Models for Vertical Federated Learning: A Survey

TL;DR

This article categorizes tree-based models in VFL into two types, i.e., feature-gathering models and label-scattering models, and provides a detailed discussion regarding their characteristics, advantages, privacy protection mechanisms, and applications.

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.

Figures (13)

• Figure 1: Communication and computation behaviors happen in a training round of Horizontal and Vertical FL.
• Figure 2: Overview of tree-based models for vertical federated learning.
• Figure 3: Comparisons between feature-gathering and label-scattering TBMs.
• Figure 4: Illustration of the privacy protection algorithms proposed by FederBoost tian2020federboost.
• Figure 5: Illustration of the privacy protection algorithms proposed by OpBoost li2022opboost.