mlsanityData to 7 Oct 2026

FederBoost: Private Federated Learning for GBDT

arXiv 2011.02796 ↗First posted 5 Nov 2020cs.CRcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 5; 2024: 8; 2025: 4; 2026: 1.

27 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  3. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  4. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  5. 2023Gradient-less Federated Gradient Boosting Trees with Learnable Learning Ratescited by 12
  6. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  7. 2022Federated Boosted Decision Trees with Differential Privacycited by 9
  8. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  9. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  10. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  11. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  12. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
15 more
  1. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  2. 2023Effective and Efficient Federated Tree Learning on Hybrid Datacited by 5
  3. 2022Federated XGBoost on Sample-Wise Non-IID Datacited by 4
  4. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
  5. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  6. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  7. 2024AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learningcited by 2
  8. 2022A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Predictioncited by 1
  9. 2024Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Datacited by 1
  10. 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
  11. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  12. 2024Attributed Graph Clustering in Collaborative Settingscited by 0
  13. 2025TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systemscited by 0
  14. 2025FedGA-Tree: Federated Decision Tree using Genetic Algorithmcited by 0
  15. 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregationcited by 0