# FederBoost: Private Federated Learning for GBDT

arXiv: https://arxiv.org/abs/2011.02796 · HTML: https://mlsanity.com/paper/2011.02796/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 27 of 27)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering](https://mlsanity.com/paper/2210.14664/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization](https://mlsanity.com/paper/2210.01318/) (2022)
- [Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates](https://mlsanity.com/paper/2304.07537/) (2023)
- [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://mlsanity.com/paper/2108.11444/) (2021)
- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [Enabling SQL-based Training Data Debugging for Federated Learning](https://mlsanity.com/paper/2108.11884/) (2021)
- [Continual Horizontal Federated Learning for Heterogeneous Data](https://mlsanity.com/paper/2203.02108/) (2022)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [VFLAIR: A Research Library and Benchmark for Vertical Federated Learning](https://mlsanity.com/paper/2310.09827/) (2023)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Eliminating Label Leakage in Tree-Based Vertical Federated Learning](https://mlsanity.com/paper/2307.10318/) (2023)
- [Effective and Efficient Federated Tree Learning on Hybrid Data](https://mlsanity.com/paper/2310.11865/) (2023)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks](https://mlsanity.com/paper/2405.02074/) (2024)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning](https://mlsanity.com/paper/2403.08383/) (2024)
- [A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction](https://mlsanity.com/paper/2201.02783/) (2022)
- [Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data](https://mlsanity.com/paper/2405.02067/) (2024)
- [How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations](https://mlsanity.com/paper/2507.01487/) (2025)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [Attributed Graph Clustering in Collaborative Settings](https://mlsanity.com/paper/2411.12329/) (2024)
- [TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems](https://mlsanity.com/paper/2506.07605/) (2025)
- [FedGA-Tree: Federated Decision Tree using Genetic Algorithm](https://mlsanity.com/paper/2506.08176/) (2025)
- [Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation](https://mlsanity.com/paper/2604.07125/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has been an emerging trend in machine learning and artificial intelligence. It allows multiple participants to collaboratively train a better global model and offers a privacy-aware paradigm for model training since it does not require participants to release their original training data. However, existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this paper, we propose a framework named FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both vertically and horizontally partitioned data. Vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. The key observation is that the whole training process of GBDT relies on the ordering of the data instead of the values. We fully implement FederBoost and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal FederBoost achieve the same level of accuracy with centralized training where all data are collected in a central server, and they are 4-5 orders of magnitude faster than the state-of-the-art solutions for federated decision tree training; hence offering practical solutions for industrial applications.