Privacy Preserving Vertical Federated Learning for Tree-based Models
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Abstract (arXiv)
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies it vertical federated learning, which tackles the scenarios where (i) collaborating organizations own data of the same set of users but with disjoint features, and (ii) only one organization holds the labels. We propose Pivot, a novel solution for privacy preserving vertical decision tree training and prediction, ensuring that no intermediate information is disclosed other than those the clients have agreed to release (i.e., the final tree model and the prediction output). Pivot does not rely on any trusted third party and provides protection against a semi-honest adversary that may compromise m-1 out of m clients. We further identify two privacy leakages when the trained decision tree model is released in plaintext and propose an enhanced protocol to mitigate them. The proposed solution can also be extended to tree ensemble models, e.g., random forest (RF) and gradient boosting decision tree (GBDT) by treating single decision trees as building blocks. Theoretical and experimental analysis suggest that Pivot is efficient for the privacy achieved.
Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2020.
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Citing papers per year: 2021: 16; 2022: 19; 2023: 19; 2024: 19; 2025: 11; 2026: 5.
89 papers.
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- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
- 2021Federated Learning on Non-IID Data: A Surveycited by 345
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
- 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
- 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
- 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
- 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
- 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
- 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
- 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
38 more
- 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
- 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
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- 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
- 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
- 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
- 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
- 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
- 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
- 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
- 2022Differentially Private Vertical Federated Clusteringcited by 10
- 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
- 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
- 2024Feature Inference Attack on Shapley Valuescited by 9
- 2021FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Searchcited by 8
- 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
- 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 8
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- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
- 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challengescited by 7
- 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
- 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
- 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6
- 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
- 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
- 2022EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Partycited by 5
- 2023Effective and Efficient Federated Tree Learning on Hybrid Datacited by 5
- 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
- 2024NeurDB: An AI-powered Autonomous Data Systemcited by 5
- 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
- 2021A Fusion-Denoising Attack on InstaHide with Data Augmentationcited by 4
- 2021A Vertical Federated Learning Framework for Horizontally Partitioned Labelscited by 4
- 2022Differentially Private Vertical Federated Learningcited by 4
- 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofscited by 4
- 2024Vertical Federated Unlearning via Backdoor Certificationcited by 4
- 2022UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworkscited by 3
50 most cited of 89 All 89 (JSON)
- 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice13 Aug 2026
- 2026Practical Anonymous Two-Party Gradient Boosting Decision Tree26 May 2026
- 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation8 Apr 2026
- 2026BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy2 Apr 2026
- 2026One-Shot Price Forecasting with Covariate-Guided Experts under Privacy Constraints17 Jan 2026
- 2025FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization30 Jul 2025
- 2025Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence6 Jul 2025
- 2025TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems9 Jun 2025
- 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos22 May 2025
- 2025Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review5 May 2025
- 2025Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach23 Apr 2025
- 2025Tree-based Models for Vertical Federated Learning: A Survey3 Apr 2025
38 more
- 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection2 Apr 2025
- 2025Scaling Probabilistic Circuits via Data Partitioning11 Mar 2025
- 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization27 Feb 2025
- 2025Federated Granger Causality Learning for Interdependent Clients with State Space Representation23 Jan 2025
- 2024Vertical Federated Unlearning via Backdoor Certification16 Dec 2024
- 2024Review of Mathematical Optimization in Federated Learning2 Dec 2024
- 2024One Node Per User: Node-Level Federated Learning for Graph Neural Networks29 Sep 2024
- 2024A Hybrid Federated Kernel Regularized Least Squares Algorithm24 Jul 2024
- 2024Feature Inference Attack on Shapley Values16 Jul 2024
- 2024Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging31 May 2024
- 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey25 May 2024
- 2024NeurDB: An AI-powered Autonomous Data System7 May 2024
- 2024A Survey on Contribution Evaluation in Vertical Federated Learning3 May 2024
- 2024Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data3 May 2024
- 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning30 Apr 2024
- 2024TabVFL: Improving Latent Representation in Vertical Federated Learning27 Apr 2024
- 2024Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning6 Apr 2024
- 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learning23 Feb 2024
- 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective6 Feb 2024
- 2024Survey of Privacy Threats and Countermeasures in Federated Learning1 Feb 2024
- 2024Decentralized Federated Learning: A Survey on Security and Privacy25 Jan 2024
- 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection19 Jan 2024
- 2024Vertical Federated Image Segmentation15 Jan 2024
- 2023Exploiting Label Skews in Federated Learning with Model Concatenation11 Dec 2023
- 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs26 Nov 2023
- 2023Effective and Efficient Federated Tree Learning on Hybrid Data18 Oct 2023
- 2023Passive Inference Attacks on Split Learning via Adversarial Regularization16 Oct 2023
- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning15 Oct 2023
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning1 Sep 2023
- 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings26 Jul 2023
- 2023SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning20 Jul 2023
- 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learning19 Jul 2023
- 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks5 Jul 2023
- 2023Federated Learning over Harmonized Data Silos15 May 2023
- 2023A Survey on Vertical Federated Learning: From a Layered Perspective4 Apr 2023
- 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties28 Mar 2023
- 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples28 Mar 2023
- 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions3 Mar 2023
50 newest of 89 All 89 (JSON)
- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
- 2021Federated Learning on Non-IID Data: A Surveycited by 345
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
- 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
- 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
- 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
- 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
- 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
- 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
- 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
12 most cited of 88 All 88 (JSON)
- 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice13 Aug 2026
- 2026Practical Anonymous Two-Party Gradient Boosting Decision Tree26 May 2026
- 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation8 Apr 2026
- 2026BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy2 Apr 2026
- 2025FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization30 Jul 2025
- 2025Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence6 Jul 2025
- 2025TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems9 Jun 2025
- 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos22 May 2025
- 2025Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review5 May 2025
- 2025Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach23 Apr 2025
- 2025Tree-based Models for Vertical Federated Learning: A Survey3 Apr 2025
- 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection2 Apr 2025
12 newest of 88 All 88 (JSON)