mlsanityData to 7 Oct 2026

A Survey on Vertical Federated Learning: From a Layered Perspective

arXiv 2304.01829 ↗First posted 4 Apr 2023cs.LG cs.AIcited by 30

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Abstract (arXiv)

Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches the description of samples using features from different parties to improve model capacity. Compared with horizontal federated learning, in most cases, VFL is applied in the commercial cooperation scenario of companies. Therefore, VFL contains tremendous business values. In the past few years, VFL has attracted more and more attention in both academia and industry. In this paper, we systematically investigate the current work of VFL from a layered perspective. From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL. Moreover, the application of VFL has covered a wide range of areas, e.g., finance, healthcare, etc. At each layer, we categorize the existing work and explore the challenges for the convenience of further research and development of VFL. Especially, we design a novel MOSP tree taxonomy to analyze the core component of VFL, i.e., secure vertical federated machine learning algorithm. Our taxonomy considers four dimensions, i.e., machine learning model (M), protection object (O), security model (S), and privacy-preserving protocol (P), and provides a comprehensive investigation.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 90 of its 245 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

90 of 245 references.

References · 90

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38 more
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50 most cited of 90 All 90 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 13; 2025: 9; 2026: 3.

30 papers.

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  1. 2024Federated Distillation: A Surveycited by 14
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2024A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directionscited by 10
  4. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  5. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  6. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  7. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  8. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  9. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  10. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  11. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  12. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
18 more
  1. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 2
  2. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
  3. 2024Federated Block-Term Tensor Regression for decentralised data analysis in healthcarecited by 1
  4. 2024Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learningcited by 1
  5. 2025Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learningcited by 1
  6. 2026Federated Imputation under Heterogeneous Feature Spacescited by 1
  7. 2024P3LS: Partial Least Squares under Privacy Preservationcited by 0
  8. 2024Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoTcited by 0
  9. 2024Active-Passive Federated Learning for Vertically Partitioned Multi-view Datacited by 0
  10. 2025Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networkscited by 0
  11. 2025Secure Visual Data Processing via Federated Learningcited by 0
  12. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  13. 2025On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attackcited by 0
  14. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  15. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
  16. 2025HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classificationcited by 0
  17. 2026Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulationcited by 0
  18. 2026Beyond Weights and Gradients: A Taxonomy of Federated Learning Messagescited by 0