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SecureBoost: A Lossless Federated Learning Framework

arXiv 1901.08755 ↗First posted 25 Jan 2019cs.LG stat.MLcited by 174

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Abstract (arXiv)

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non-privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that require centralized data and thus it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.

Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2019.

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Citing papers per year: 2021: 34; 2022: 42; 2023: 33; 2024: 34; 2025: 22; 2026: 9.

174 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
  3. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  4. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  5. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  6. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  7. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  8. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  9. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  10. 2022A Survey on Heterogeneous Federated Learningcited by 42
  11. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  12. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
38 more
  1. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  2. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  3. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  4. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  5. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  6. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  7. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  8. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  9. 2023Federated Domain Generalization: A Surveycited by 25
  10. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  11. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  12. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  13. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  14. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  15. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  16. 2024FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Modelscited by 19
  17. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  18. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  19. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  20. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  21. 2021Scalable federated machine learning with FEDncited by 16
  22. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  23. 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
  24. 2021FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learningcited by 15
  25. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  26. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  27. 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
  28. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  29. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  30. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  31. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  32. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  33. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  34. 2023Gradient-less Federated Gradient Boosting Trees with Learnable Learning Ratescited by 12
  35. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  36. 2021A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learningcited by 11
  37. 2023Towards Interpretable Federated Learningcited by 11
  38. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10

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