Large-Scale Secure XGB for Vertical Federated Learning

Understand

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.

• Under such situation, Federated Learning (FL) appears to facilitate privacy-preserving joint modeling among multiple parties.
• Although many federated algorithms have been extensively studied, there is still a lack of secure and practical gradient tree boosting models (e.g., XGB) in literature.
• In this paper, we aim to build large-scale secure XGB under vertically federated learning setting.