An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization

TL;DR

This paper introduces a secure, efficient multi-party federated XGBoost framework that addresses data privacy, reduces communication overhead, and improves performance over existing models using secret sharing and distributed optimization.

Contribution

It proposes a novel lossless federated XGBoost framework with security guarantees, reshaping split criterion calculation and solving leaf weight issues via distributed optimization.

Findings

Outperforms state-of-the-art models on benchmark datasets

Ensures data privacy with a lossless security guarantee

Reduces communication and computation overheads

Abstract

XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models either have data leakage issues or are only applicable to the two-party setting with heavy communication and computation overheads. In this paper, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost's split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. Remarkably, a thorough analysis of model security is provided as well, and multiple numerical results showcase the superiority of the proposed FedXGB compared with the state-of-the-art models on benchmark datasets.