# A Survey on Vertical Federated Learning: From a Layered Perspective

arXiv: https://arxiv.org/abs/2304.01829 · HTML: https://mlsanity.com/paper/2304.01829/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 90 of its 245 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 30 of 90)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Feature Inference Attack on Model Predictions in Vertical Federated Learning](https://mlsanity.com/paper/2010.10152/) (2020)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)

## Built on by (most cited first, 30 of 30)

- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation](https://mlsanity.com/paper/2406.12815/) (2024)
- [Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data](https://mlsanity.com/paper/2410.17986/) (2024)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [Eliminating Label Leakage in Tree-Based Vertical Federated Learning](https://mlsanity.com/paper/2307.10318/) (2023)
- [Online Vertical Federated Learning for Cooperative Spectrum Sensing](https://mlsanity.com/paper/2312.11363/) (2023)
- [A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning](https://mlsanity.com/paper/2309.09977/) (2023)
- [Communication-efficient Vertical Federated Learning via Compressed Error Feedback](https://mlsanity.com/paper/2406.14420/) (2024)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [Hierarchical Federated Learning Incentivization for Gas Usage Estimation](https://mlsanity.com/paper/2307.00233/) (2023)
- [PackVFL: Efficient HE Packing for Vertical Federated Learning](https://mlsanity.com/paper/2405.00482/) (2024)
- [Federated Block-Term Tensor Regression for decentralised data analysis in healthcare](https://mlsanity.com/paper/2412.06815/) (2024)
- [Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning](https://mlsanity.com/paper/2412.11689/) (2024)
- [Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learning](https://mlsanity.com/paper/2511.18887/) (2025)
- [Federated Imputation under Heterogeneous Feature Spaces](https://mlsanity.com/paper/2605.16099/) (2026)
- [P3LS: Partial Least Squares under Privacy Preservation](https://mlsanity.com/paper/2401.14884/) (2024)
- [Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT](https://mlsanity.com/paper/2404.00466/) (2024)
- [Active-Passive Federated Learning for Vertically Partitioned Multi-view Data](https://mlsanity.com/paper/2409.04111/) (2024)
- [Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks](https://mlsanity.com/paper/2502.02874/) (2025)
- [Secure Visual Data Processing via Federated Learning](https://mlsanity.com/paper/2502.06889/) (2025)
- [Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios](https://mlsanity.com/paper/2505.11035/) (2025)
- [On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attack](https://mlsanity.com/paper/2507.02376/) (2025)
- [VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning](https://mlsanity.com/paper/2507.14625/) (2025)
- [VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking](https://mlsanity.com/paper/2507.14629/) (2025)
- [HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification](https://mlsanity.com/paper/2512.10701/) (2025)
- [Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation](https://mlsanity.com/paper/2602.15996/) (2026)
- [Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages](https://mlsanity.com/paper/2606.16891/) (2026)

## 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.