# Vertical Federated Learning: A Structured Literature Review

arXiv: https://arxiv.org/abs/2212.00622 · HTML: https://mlsanity.com/paper/2212.00622/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 168 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 30 of 59)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [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 datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [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)
- [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)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://mlsanity.com/paper/2005.11903/) (2020)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator](https://mlsanity.com/paper/1911.09824/) (2019)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation](https://mlsanity.com/paper/2004.08945/) (2020)
- [FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning](https://mlsanity.com/paper/2006.16742/) (2020)
- [From Federated Learning to Federated Neural Architecture Search: A Survey](https://mlsanity.com/paper/2009.05868/) (2020)
- [A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://mlsanity.com/paper/1912.00513/) (2019)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)

## Built on by (most cited first, 17 of 17)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2502.08160/) (2025)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications](https://mlsanity.com/paper/2510.17901/) (2025)
- [Networked Information Aggregation for Binary Classification](https://mlsanity.com/paper/2605.01082/) (2026)
- [Federated learning in food research](https://mlsanity.com/paper/2406.06202/) (2024)
- [VFL-RPS: Relevant Participant Selection in Vertical Federated Learning](https://mlsanity.com/paper/2502.14375/) (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)
- [VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking](https://mlsanity.com/paper/2507.14629/) (2025)
- [Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation](https://mlsanity.com/paper/2602.15996/) (2026)
- [HybridFL: A Federated Learning Approach for Financial Crime Detection](https://mlsanity.com/paper/2602.19207/) (2026)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)
- [Expected Gain-based Escalation in Vertical Federated Learning](https://mlsanity.com/paper/2606.31331/) (2026)
- [Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates](https://mlsanity.com/paper/2609.04815/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain.