# Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly

arXiv: https://arxiv.org/abs/2502.08160 · HTML: https://mlsanity.com/paper/2502.08160/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 71 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 30 of 43)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [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)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [Self-Supervised Multimodal Learning: A Survey](https://mlsanity.com/paper/2304.01008/) (2023)
- [Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes](https://mlsanity.com/paper/2312.06353/) (2023)
- [Free-riders in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/1911.12560/) (2019)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation](https://mlsanity.com/paper/2303.12233/) (2023)
- [When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control](https://mlsanity.com/paper/2008.08753/) (2020)
- [FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training](https://mlsanity.com/paper/2008.10838/) (2020)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [Practical Vertical Federated Learning with Unsupervised Representation Learning](https://mlsanity.com/paper/2208.10278/) (2022)
- [Fair and efficient contribution valuation for vertical federated learning](https://mlsanity.com/paper/2201.02658/) (2022)
- [A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning](https://mlsanity.com/paper/2208.08934/) (2022)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization](https://mlsanity.com/paper/2109.12519/) (2021)
- [Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning](https://mlsanity.com/paper/2112.05409/) (2021)
- [Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples](https://mlsanity.com/paper/2303.16270/) (2023)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)
- [BadVFL: Backdoor Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2304.08847/) (2023)
- [GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations](https://mlsanity.com/paper/2106.01425/) (2021)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data](https://mlsanity.com/paper/2410.17986/) (2024)

## Built on by (most cited first, 4 of 4)

- [FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing](https://mlsanity.com/paper/2505.08325/) (2025)
- [Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios](https://mlsanity.com/paper/2505.11035/) (2025)
- [Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference](https://mlsanity.com/paper/2607.20124/) (2026)
- [A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression](https://mlsanity.com/paper/2609.22654/) (2026)

## Abstract (arXiv)

Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL systems in real-world applications remains limited. To investigate the gap between existing VFL research and practical deployment, this survey analyzes the real-world data distributions in potential VFL applications and identifies four key findings that highlight this gap. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Our comprehensive review of existing VFL algorithms reveals that some common practical VFL scenarios have few or no viable solutions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.