# Vertical Federated Learning: Taxonomies, Threats, and Prospects

arXiv: https://arxiv.org/abs/2302.01550 · HTML: https://mlsanity.com/paper/2302.01550/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 97 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2007.10760/) (2020)
- [Feature Inference Attack on Model Predictions in Vertical Federated Learning](https://mlsanity.com/paper/2010.10152/) (2020)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [Line Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2010.10046/) (2020)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [Inductive Entity Representations from Text via Link Prediction](https://mlsanity.com/paper/2010.03496/) (2020)
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://mlsanity.com/paper/2005.11903/) (2020)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1912.05897/) (2019)
- [Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption](https://mlsanity.com/paper/2104.06824/) (2021)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Backdoor attacks and defenses in feature-partitioned collaborative learning](https://mlsanity.com/paper/2007.03608/) (2020)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [Asymmetrical Vertical Federated Learning](https://mlsanity.com/paper/2004.07427/) (2020)

## Built on by (most cited first, 8 of 8)

- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2502.08160/) (2025)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems](https://mlsanity.com/paper/2512.23132/) (2025)
- [A Bargaining-based Approach for Feature Trading in Vertical Federated Learning](https://mlsanity.com/paper/2402.15247/) (2024)
- [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)
- [CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning](https://mlsanity.com/paper/2606.08027/) (2026)

## Abstract (arXiv)

Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for processing. Instead, local models are trained with local data; the models are then shared and combined. This approach preserves data privacy as locally trained models are shared instead of the raw data themselves. Broadly, FL can be divided into horizontal federated learning (HFL) and vertical federated learning (VFL). For the former, different parties hold different samples over the same set of features; for the latter, different parties hold different feature data belonging to the same set of samples. In a number of practical scenarios, VFL is more relevant than HFL as different companies (e.g., bank and retailer) hold different features (e.g., credit history and shopping history) for the same set of customers. Although VFL is an emerging area of research, it is not well-established compared to HFL. Besides, VFL-related studies are dispersed, and their connections are not intuitive. Thus, this survey aims to bring these VFL-related studies to one place. Firstly, we classify existing VFL structures and algorithms. Secondly, we present the threats from security and privacy perspectives to VFL. Thirdly, for the benefit of future researchers, we discussed the challenges and prospects of VFL in detail.