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Multi-Participant Multi-Class Vertical Federated Learning

arXiv 2001.11154 ↗First posted 30 Jan 2020cs.LG stat.MLcited by 47

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Abstract (arXiv)

Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL problems involving multiple parties. Extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacypreserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. Experiment results on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match multi-class classification performance of existing approaches.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2020.

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Citing papers per year: 2021: 13; 2022: 12; 2023: 9; 2024: 10; 2025: 3.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  4. 2022A Survey on Heterogeneous Federated Learningcited by 42
  5. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  6. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  7. 2021Federated Graph Learning -- A Position Papercited by 34
  8. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  9. 2024Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metricscited by 25
  10. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  11. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  12. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
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  1. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  2. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  3. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  4. 2021Federated Multi-View Learning for Private Medical Data Integration and Analysiscited by 12
  5. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
  6. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  7. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  8. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  9. 2024A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directionscited by 10
  10. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  11. 2022A Review of Federated Learning in Energy Systemscited by 9
  12. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  13. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  14. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
  15. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  16. 2023Label Inference Attack against Split Learning under Regression Settingcited by 7
  17. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  18. 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6
  19. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  20. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  21. 2022Differentially Private Vertical Federated Learningcited by 4
  22. 2023FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendationcited by 4
  23. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  24. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  25. 2021Management of Resource at the Network Edge for Federated Learningcited by 2
  26. 2022CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approachcited by 2
  27. 2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
  28. 2024S2NeRF: Privacy-preserving Training Framework for NeRFcited by 1
  29. 2021FedProf: Selective Federated Learning with Representation Profilingcited by 0
  30. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  31. 2024Vertical Federated Image Segmentationcited by 0
  32. 2024TablePuppet: A Generic Framework for Relational Federated Learningcited by 0
  33. 2025Reliable Imputed-Sample Assisted Vertical Federated Learningcited by 0
  34. 2025Principles and Components of Federated Learning Architecturescited by 0
  35. 2025ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learningcited by 0