mlsanityData to 7 Oct 2026

FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training

arXiv 2008.10838 ↗First posted 25 Aug 2020cs.LG stat.MLcited by 32

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Abstract (arXiv)

Federated learning allows multiple parties to build machine learning models collaboratively without exposing data. In particular, vertical federated learning (VFL) enables participating parties to build a joint machine learning model based on distributed features of aligned samples. However, VFL requires all parties to share a sufficient amount of aligned samples. In reality, the set of aligned samples may be small, leaving the majority of the non-aligned data unused. In this article, we propose Federated Cross-view Training (FedCVT), a semi-supervised learning approach that improves the performance of the VFL model with limited aligned samples. More specifically, FedCVT estimates representations for missing features, predicts pseudo-labels for unlabeled samples to expand the training set, and trains three classifiers jointly based on different views of the expanded training set to improve the VFL model's performance. FedCVT does not require parties to share their original data and model parameters, thus preserving data privacy. We conduct experiments on NUS-WIDE, Vehicle, and CIFAR10 datasets. The experimental results demonstrate that FedCVT significantly outperforms vanilla VFL that only utilizes aligned samples. Finally, we perform ablation studies to investigate the contribution of each component of FedCVT to the performance of FedCVT. Code is available at https://github.com/yankang18/FedCVT

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 9; 2024: 7; 2025: 8; 2026: 1.

32 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  6. 2023Federated Domain Generalization: A Surveycited by 25
  7. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  8. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  9. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  10. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  11. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  12. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
20 more
  1. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 9
  2. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  3. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  4. 2023FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learningcited by 6
  5. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  6. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  7. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  8. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  9. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  10. 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Treecited by 2
  11. 2024Contrastive Federated Learning with Tabular Data Siloscited by 2
  12. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  13. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1
  14. 2024Vertical Federated Learning Hybrid Local Pre-trainingcited by 1
  15. 2025Reliable Imputed-Sample Assisted Vertical Federated Learningcited by 0
  16. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  17. 2025Event-Driven Online Vertical Federated Learningcited by 0
  18. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0
  19. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  20. 2026Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learningcited by 0