mlsanityData to 7 Oct 2026

Federated Forest

arXiv 1905.10053 ↗First posted 24 May 2019cs.LG stat.MLcited by 49

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Abstract (arXiv)

Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law. Such data islands situation and data privacy & security are two major challenges for applications of artificial intelligence. In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i.e., achieving the same level of accuracy as the non-privacy-preserving approach. Based on it, we developed a secure cross-regional machine learning system that allows a learning process to be jointly trained over different regions' clients with the same user samples but different attribute sets, processing the data stored in each of them without exchanging their raw data. A novel prediction algorithm was also proposed which could largely reduce the communication overhead. Experiments on both real-world and UCI data sets demonstrate the performance of the Federated Forest is as accurate as the non-federated version. The efficiency and robustness of our proposed system had been verified. Overall, our model is practical, scalable and extensible for real-life tasks.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 11; 2023: 6; 2024: 11; 2025: 6; 2026: 3.

49 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022A Survey on Heterogeneous Federated Learningcited by 42
  3. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  4. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  5. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  6. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  7. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  8. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  9. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  10. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  11. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  12. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
37 more
  1. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  2. 2022Differentially Private Vertical Federated Clusteringcited by 10
  3. 2021An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimizationcited by 9
  4. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  5. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  6. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  7. 2021Efficient Batch Homomorphic Encryption for Vertically Federated XGBoostcited by 6
  8. 2022An Efficient and Robust System for Vertically Federated Random Forestcited by 6
  9. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  10. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  11. 2022FedGBF: An efficient vertical federated learning framework via gradient boosting and baggingcited by 5
  12. 2024An Interpretable Client Decision Tree Aggregation process for Federated Learningcited by 5
  13. 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
  14. 2021Accelerating Federated Learning with a Global Biased Optimisercited by 4
  15. 2022Federated XGBoost on Sample-Wise Non-IID Datacited by 4
  16. 2022Differentially Private Vertical Federated Learningcited by 4
  17. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  18. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  19. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  20. 2021Privacy-Preserving Federated Learning on Partitioned Attributescited by 2
  21. 2024Blind Federated Learning without initial modelcited by 2
  22. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  23. 2023Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computationcited by 1
  24. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
  25. 2024Federated Learning for Tabular Data using TabNet: A Vehicular Use-Casecited by 1
  26. 2024Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Datacited by 1
  27. 2024Concurrent vertical and horizontal federated learning with fuzzy cognitive mapscited by 1
  28. 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
  29. 2021Privacy preserving n-party scalar product protocolcited by 0
  30. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  31. 2025Federated Learning with Discriminative Naive Bayes Classifiercited by 0
  32. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  33. 2025Financial Data Analysis with Robust Federated Logistic Regressioncited by 0
  34. 2025FedGA-Tree: Federated Decision Tree using Genetic Algorithmcited by 0
  35. 2026Principled Federated Random Forests for Heterogeneous Datacited by 0
  36. 2026Towards Explainable Federated Learning: Understanding the Impact of Differential Privacycited by 0
  37. 2026A Machine Learning API for Earth Observation Data Cubes Based on openEOcited by 0