mlsanityData to 7 Oct 2026

Vertical Federated Learning without Revealing Intersection Membership

arXiv 2106.05508 ↗First posted 10 Jun 2021cs.LG cs.AIcited by 19

Not on the map. Why?

Abstract (arXiv)

Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model. To prepare the training data, vFL needs to identify the common data entities shared by all parties. It is usually achieved by Private Set Intersection (PSI) which identifies the intersection of training samples from all parties by using personal identifiable information (e.g. email) as sample IDs to align data instances. As a result, PSI would make sample IDs of the intersection visible to all parties, and therefore each party can know that the data entities shown in the intersection also appear in the other parties, i.e. intersection membership. However, in many real-world privacy-sensitive organizations, e.g. banks and hospitals, revealing membership of their data entities is prohibited. In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself. Instead of identifying the intersection of all training samples, our PSU protocol generates the union of samples as training instances. In addition, we propose strategies to generate synthetic features and labels to handle samples that belong to the union but not the intersection. Through extensive experiments on two real-world datasets, we show our framework can protect the privacy of the intersection membership while maintaining the model utility.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

18 of 39 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Wide & Deep Learning for Recommender Systemscited by 745
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  4. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  5. 2018Spectral Signatures in Backdoor Attackscited by 457
  6. 2019Measuring Calibration in Deep Learningcited by 443
  7. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  8. 2018Distributed learning of deep neural network over multiple agentscited by 277
  9. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  10. 2020Acceleration for Compressed Gradient Descent in Distributed and Federated Optimizationcited by 90
  11. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  12. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
6 more
  1. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  2. 2020Federated Accelerated Stochastic Gradient Descentcited by 85
  3. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  4. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  5. 2020SplitNN-driven Vertical Partitioningcited by 42
  6. 2020Asymmetrical Vertical Federated Learningcited by 26

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 5; 2024: 5; 2025: 0; 2026: 2.

19 papers.

Sort:

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  3. 2023Towards Personalized Federated Learning via Heterogeneous Model Reassemblycited by 31
  4. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  5. 2022Differentially Private Label Protection in Split Learningcited by 25
  6. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  7. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  8. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  9. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  10. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  11. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  12. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
7 more
  1. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  2. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  3. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
  4. 2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
  5. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  6. 2026Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifierscited by 0
  7. 2026Practical Anonymous Two-Party Gradient Boosting Decision Treecited by 0