# Differentially Private Vertical Federated Learning

arXiv: https://arxiv.org/abs/2211.06782 · HTML: https://mlsanity.com/paper/2211.06782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 54 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [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)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (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)
- [Feature Inference Attack on Model Predictions in Vertical Federated Learning](https://mlsanity.com/paper/2010.10152/) (2020)
- [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022)
- [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)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (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)
- [Hybrid Differentially Private Federated Learning on Vertically Partitioned Data](https://mlsanity.com/paper/2009.02763/) (2020)
- [Defending against Reconstruction Attack in Vertical Federated Learning](https://mlsanity.com/paper/2107.09898/) (2021)
- [Differential Privacy Meets Federated Learning under Communication Constraints](https://mlsanity.com/paper/2101.12240/) (2021)
- [Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things](https://mlsanity.com/paper/2012.04436/) (2020)
- [Deep Leakage from Model in Federated Learning](https://mlsanity.com/paper/2206.04887/) (2022)

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

- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization](https://mlsanity.com/paper/2306.16077/) (2023)
- [Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings](https://mlsanity.com/paper/2308.02362/) (2023)
- [Event-Driven Online Vertical Federated Learning](https://mlsanity.com/paper/2506.14911/) (2025)

## Abstract (arXiv)

A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applications, however with large costs and human efforts to label training data. Recent advancements in federated learning (FL) allow multiple data owners or organisations to collaboratively train a machine learning model without sharing raw data. In this light, vertical FL allows organisations to build a global model when the participating organisations have vertically partitioned data. Further, in the vertical FL setting the participating organisation generally requires fewer resources compared to sharing data directly, enabling lightweight and scalable distributed training solutions. However, privacy protection in vertical FL is challenging due to the communication of intermediate outputs and the gradients of model update. This invites adversary entities to infer other organisations underlying data. Thus, in this paper, we aim to explore how to protect the privacy of individual organisation data in a differential privacy (DP) setting. We run experiments with different real-world datasets and DP budgets. Our experimental results show that a trade-off point needs to be found to achieve a balance between the vertical FL performance and privacy protection in terms of the amount of perturbation noise.