mlsanityData to 14 Oct 2026

PIVODL: Privacy-preserving vertical federated learning over distributed labels

arXiv 2108.11444 ↗First posted 25 Aug 2021cs.CR cs.AI cs.DCcited by 9

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Non-parametric models like gradient boosting decision trees (GBDT) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this work, we propose a secure vertical FL framework, named PIVODL, to train GBDT with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

20 of 47 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  7. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  8. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  10. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  11. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  12. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
8 more
  1. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  2. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  3. 2020Asymmetrical Vertical Federated Learningcited by 26
  4. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  5. 2019A Communication Efficient Collaborative Learning Framework for Distributed Featurescited by 21
  6. 2018Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learningcited by 15
  7. 2020Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learningcited by 13
  8. 2019An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learningcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

9 papers.

  1. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  2. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  3. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  4. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  5. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  6. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  7. 2024MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasetscited by 1
  8. 2025OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learningcited by 1
  9. 2026SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacycited by 0