# Practical Vertical Federated Learning with Unsupervised Representation Learning

arXiv: https://arxiv.org/abs/2208.10278 · HTML: https://mlsanity.com/paper/2208.10278/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 27 of 27)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [Distilled One-Shot Federated Learning](https://mlsanity.com/paper/2009.07999/) (2020)
- [Unsupervised Learning by Predicting Noise](https://mlsanity.com/paper/1704.05310/) (2017)
- [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)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees](https://mlsanity.com/paper/2002.10940/) (2020)
- [FLAME: Differentially Private Federated Learning in the Shuffle Model](https://mlsanity.com/paper/2009.08063/) (2020)
- [Asymmetrical Vertical Federated Learning](https://mlsanity.com/paper/2004.07427/) (2020)
- [Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case](https://mlsanity.com/paper/1802.04085/) (2018)
- [Learning Privately over Distributed Features: An ADMM Sharing Approach](https://mlsanity.com/paper/1907.07735/) (2019)
- [Achieving Differential Privacy in Vertically Partitioned Multiparty Learning](https://mlsanity.com/paper/1911.04587/) (2019)
- [The Disruptions of 5G on Data-driven Technologies and Applications](https://mlsanity.com/paper/1909.08096/) (2019)

## Built on by (most cited first, 19 of 19)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples](https://mlsanity.com/paper/2303.16270/) (2023)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data](https://mlsanity.com/paper/2410.17986/) (2024)
- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2502.08160/) (2025)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data](https://mlsanity.com/paper/2403.03871/) (2024)
- [TabVFL: Improving Latent Representation in Vertical Federated Learning](https://mlsanity.com/paper/2404.17990/) (2024)
- [Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning](https://mlsanity.com/paper/2602.20593/) (2026)
- [Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios](https://mlsanity.com/paper/2505.11035/) (2025)
- [WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos](https://mlsanity.com/paper/2505.16635/) (2025)
- [Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data](https://mlsanity.com/paper/2507.22488/) (2025)

## Abstract (arXiv)

As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications. Federated learning emerges as a new learning paradigm that enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Vertical federated learning, where each party owns different features of the same set of samples and only a single party has the label, is an important and challenging topic in federated learning. Communication costs among different parties have been a major hurdle for practical vertical learning systems. In this paper, we propose a novel communication-efficient vertical federated learning algorithm named FedOnce, which requires only one-shot communication among parties. To improve model accuracy and provide privacy guarantee, FedOnce features unsupervised learning representations in the federated setting and privacy-preserving techniques based on moments accountant. The comprehensive experiments on 10 datasets demonstrate that FedOnce achieves close performance compared to state-of-the-art vertical federated learning algorithms with much lower communication costs. Meanwhile, our privacy-preserving technique significantly outperforms the state-of-the-art approaches under the same privacy budget.