mlsanityData to 14 Oct 2026

Practical Federated Gradient Boosting Decision Trees

arXiv 1911.04206 ↗First posted 11 Nov 2019cs.LG stat.MLcited by 54

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Abstract (arXiv)

Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. However, existing studies are not efficient or effective enough for practical use. They suffer either from the inefficiency due to the usage of costly data transformations such as secret sharing and homomorphic encryption, or from the low model accuracy due to differential privacy designs. In this paper, we study a practical federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. We prove that our framework is secure without exposing the original record to other parties, while the computation overhead in the training process is kept low. Our experimental studies show that, compared with normal training with the local data of each party, our approach can significantly improve the predictive accuracy, and achieve comparable accuracy to the original GBDT with the data from all parties.

Key facts. Cited by 54 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2019.

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Citing papers per year: 2021: 11; 2022: 13; 2023: 10; 2024: 12; 2025: 7; 2026: 1.

54 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 766
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  4. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 109
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  7. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 54
  8. 2022A Survey on Heterogeneous Federated Learningcited by 42
  9. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  10. 2024Membership Inference Attacks and Defenses in Federated Learning: A Surveycited by 31
  11. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 30
  12. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
38 more
  1. 2023A Survey on Decentralized Federated Learningcited by 27
  2. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  3. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  4. 2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
  5. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  6. 2023Gradient-less Federated Gradient Boosting Trees with Learnable Learning Ratescited by 12
  7. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
  8. 2021FedXGBoost: Privacy-Preserving XGBoost for Federated Learningcited by 9
  9. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  10. 2021Federated Learning Versus Classical Machine Learning: A Convergence Comparisoncited by 7
  11. 2022An Empirical Study of Personalized Federated Learningcited by 6
  12. 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalercited by 6
  13. 2021Federated Functional Gradient Boostingcited by 5
  14. 2023Effective and Efficient Federated Tree Learning on Hybrid Datacited by 5
  15. 2024An Interpretable Client Decision Tree Aggregation process for Federated Learningcited by 5
  16. 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
  17. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  18. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  19. 2022Federated XGBoost on Sample-Wise Non-IID Datacited by 4
  20. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  21. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
  22. 2024Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matchingcited by 4
  23. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  24. 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Treecited by 2
  25. 2025Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approachcited by 2
  26. 2025Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregationcited by 2
  27. 2022A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Predictioncited by 1
  28. 2023FedSpectral+: Spectral Clustering using Federated Learningcited by 1
  29. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  30. 2024Federated Learning for Tabular Data using TabNet: A Vehicular Use-Casecited by 1
  31. 2025DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learningcited by 1
  32. 2025Distributionally Robust Federated Learning with Client Drift Minimizationcited by 1
  33. 2022Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Modelscited by 0
  34. 2023The Applicability of Federated Learning to Official Statisticscited by 0
  35. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  36. 2024Collaborative Multi-source Domain Adaptation Through Optimal Transportcited by 0
  37. 2024A Hybrid Federated Kernel Regularized Least Squares Algorithmcited by 0
  38. 2024Evaluating the Potential of Federated Learning for Maize Leaf Disease Predictioncited by 0

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