mlsanityData to 7 Oct 2026

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation

arXiv 2301.12623 ↗First posted 30 Jan 2023cs.DC cs.CR cs.LGcited by 9

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Abstract (arXiv)

Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. Strong privacy-preserving capabilities about private features and labels are theoretically proved (in Theorems 1 and 2). Extensive experimental result s with different datasets and network architectures also justify the superiority of FedPass against existing methods in light of its near-optimal trade-off between privacy and model performance.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

23 of 40 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  6. 2017Sparse Communication for Distributed Gradient Descentcited by 243
  7. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  8. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  9. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  10. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  11. 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
  12. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
11 more
  1. 2019[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attackscited by 82
  2. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  3. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  4. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 33
  5. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  6. 2020Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attackscited by 32
  7. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  8. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  9. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  10. 2022A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learningcited by 10
  11. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7

Built on by

Cited by year (2026 to 7 Oct)

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

9 papers.

  1. 2023Grounding Foundation Models through Federated Transfer Learning: A General Frameworkcited by 24
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2024FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learningcited by 10
  4. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  5. 2023Temporal Gradient Inversion Attacks with Robust Optimizationcited by 4
  6. 2024LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscationcited by 1
  7. 2025Graph Privacy: A Heterogeneous Federated GNN for Trans-Border Financial Data Circulationcited by 0
  8. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
  9. 2026CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learningcited by 0