mlsanityData to 7 Oct 2026

Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

arXiv 2303.18178 ↗First posted 28 Mar 2023cs.CR cs.LGcited by 7

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Abstract (arXiv)

Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance. Existing VFL approaches, however, have two major vulnerabilities when passive parties unexpectedly quit in the deployment phase of VFL - severe performance degradation and intellectual property (IP) leakage of the active party's labels. In this paper, we propose Party-wise Dropout to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called DIMIP to protect the active party's IP in the deployment phase. We evaluate our proposed methods on multiple datasets against different inference attacks. The results show that Party-wise Dropout effectively maintains model performance after the passive party quits, and DIMIP successfully disguises label information from the passive party's feature extractor, thereby mitigating IP leakage.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 42 of its 66 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

42 of 66 references.

References · 42

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  7. 2019Federated Machine Learning: Concept and Applicationscited by 409
  8. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  9. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  10. 2018Knockoff Nets: Stealing Functionality of Black-Box Modelscited by 285
  11. 2020CLUB: A Contrastive Log-ratio Upper Bound of Mutual Informationcited by 233
  12. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
30 more
  1. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  2. 2018PRADA: Protecting against DNN Model Stealing Attackscited by 159
  3. 2020Data-Free Model Extractioncited by 121
  4. 2021Deep Learning with Label Differential Privacycited by 113
  5. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  6. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  7. 2020Provable Defense against Privacy Leakage in Federated Learning from Representation Perspectivecited by 94
  8. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  9. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  10. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  11. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  12. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  13. 2019Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attackscited by 79
  14. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  15. 2018Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Datacited by 69
  16. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  17. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  18. 2019Federated Forestcited by 49
  19. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  20. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 37
  21. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  22. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  23. 2020Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluationscited by 30
  24. 2020Asymmetrical Vertical Federated Learningcited by 26
  25. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  26. 2019A framework for the extraction of Deep Neural Networks by leveraging public datacited by 22
  27. 2022Flexible Vertical Federated Learning with Heterogeneous Partiescited by 20
  28. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  29. 2019Learning Privately over Distributed Features: An ADMM Sharing Approachcited by 14
  30. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6

Built on by

Cited by year

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

7 papers.

  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  3. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  4. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  5. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
  6. 2025Reliable Vertical Federated Learning in 5G Core Network Architecturecited by 1
  7. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0