mlsanityData to 14 Oct 2026

Tree-based Models for Vertical Federated Learning: A Survey

arXiv 2504.02285 ↗First posted 3 Apr 2025cs.LG cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability, which inspired people to apply them in vertical federated learning (VFL) scenarios in recent years. In this paper, we conduct a comprehensive study to give an overall picture of applying tree-based models in VFL, from the perspective of their communication and computation protocols. We categorize tree-based models in VFL into two types, i.e., feature-gathering models and label-scattering models, and provide a detailed discussion regarding their characteristics, advantages, privacy protection mechanisms, and applications. This study also focuses on the implementation of tree-based models in VFL, summarizing several design principles for better satisfying various requirements from both academic research and industrial deployment. We conduct a series of experiments to provide empirical observations on the differences and advances of different types of tree-based models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 48 of its 115 references that are in the map. First posted on arXiv 3 Apr 2025.

Builds on

48 of 115 references.

References · 48

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2016Deep Learning with Differential Privacycited by 2,728
  6. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  9. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  11. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  12. 2018Distributed learning of deep neural network over multiple agentscited by 279
36 more
  1. 2018CatBoost: gradient boosting with categorical features supportcited by 208
  2. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  3. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  4. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  5. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  6. 2021Federated Learning for Open Bankingcited by 141
  7. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  8. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  9. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  10. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  11. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  12. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  13. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
  14. 2019Federated Forestcited by 49
  15. 2019Local Differential Privacy for Deep Learningcited by 46
  16. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  17. 2020Local Differential Privacy and Its Applications: A Comprehensive Surveycited by 41
  18. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 36
  19. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  20. 2019Interpret Federated Learning with Shapley Valuescited by 33
  21. 2020When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Controlcited by 32
  22. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  23. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  24. 2020Large-Scale Secure XGB for Vertical Federated Learningcited by 26
  25. 2022Exploiting Data Sparsity in Secure Cross-Platform Social Recommendationcited by 17
  26. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  27. 2013Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policiescited by 12
  28. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  29. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
  30. 2021An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimizationcited by 9
  31. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  32. 2022An Efficient and Robust System for Vertically Federated Random Forestcited by 6
  33. 2022FedGBF: An efficient vertical federated learning framework via gradient boosting and baggingcited by 5
  34. 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
  35. 2023Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tablescited by 3
  36. 2023Level Up: Private Non-Interactive Decision Tree Evaluation using Levelled Homomorphic Encryptioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdatacited by 0
  2. 2026CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classificationcited by 0
  3. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0