FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels

Rui Wang, Oguzhan Ersoy, Hangyu Zhu, Yaochu Jin, Kaitai Liang

Research output: Contribution to journalArticleScientificpeer-review


Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation and efficiency of training. However, there is a fundamental lack of research on how to conduct VFL securely over distributed labels. This work is the first to fill this gap by designing a novel protocol, called FEVERLESS, based on XGBoost. FEVERLESS leverages secure aggregation via information masking technique and global differential privacy provided by a fairly and randomly selected noise leader to prevent private information from being leaked in the training process. Furthermore, it provides label and data privacy against honest-but-curious adversaries even in the case of collusion of <inline-formula><tex-math notation="LaTeX">$n - 2$</tex-math></inline-formula> out of n clients. We present a comprehensive security and efficiency analysis for our design, and the empirical results from our experiments demonstrate that FEVERLESS is fast and secure. In particular, it outperforms the solution based on additive homomorphic encryption in runtime cost and provides better accuracy than the local differential privacy approach.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Big Data
Publication statusE-pub ahead of print - 2022


  • Vertical federated learning
  • XGBoost
  • privacy preservation
  • secure aggregation
  • differential privacy


Dive into the research topics of 'FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels'. Together they form a unique fingerprint.

Cite this