# Asymmetrical Vertical Federated Learning

arXiv: https://arxiv.org/abs/2004.07427 · HTML: https://mlsanity.com/paper/2004.07427/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 26 of 26)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://mlsanity.com/paper/2104.00489/) (2021)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [Vertical Federated Learning without Revealing Intersection Membership](https://mlsanity.com/paper/2106.05508/) (2021)
- [Practical Vertical Federated Learning with Unsupervised Representation Learning](https://mlsanity.com/paper/2208.10278/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Federated Multi-View Learning for Private Medical Data Integration and Analysis](https://mlsanity.com/paper/2105.01603/) (2021)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Residue-based Label Protection Mechanisms in Vertical Logistic Regression](https://mlsanity.com/paper/2205.04166/) (2022)
- [BadVFL: Backdoor Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2304.08847/) (2023)
- [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://mlsanity.com/paper/2108.11444/) (2021)
- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [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)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Secure Vertical Federated Learning Under Unreliable Connectivity](https://mlsanity.com/paper/2305.16794/) (2023)
- [Efficient Vertical Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.11236/) (2023)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems](https://mlsanity.com/paper/2401.02450/) (2024)
- [Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction](https://mlsanity.com/paper/2202.05139/) (2022)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [De-VertiFL: A Solution for Decentralized Vertical Federated Learning](https://mlsanity.com/paper/2410.06127/) (2024)
- [SecureSplit: Mitigating Backdoor Attacks in Split Learning](https://mlsanity.com/paper/2601.14054/) (2026)

## Abstract (arXiv)

Federated learning is a distributed machine learning method that aims to preserve the privacy of sample features and labels. In a federated learning system, ID-based sample alignment approaches are usually applied with few efforts made on the protection of ID privacy. In real-life applications, however, the confidentiality of sample IDs, which are the strongest row identifiers, is also drawing much attention from many participants. To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrical vertical federated learning and illustrates the way to protect sample IDs. The standard private set intersection protocol is adapted to achieve the asymmetrical ID alignment phase in an asymmetrical vertical federated learning system. Correspondingly, a Pohlig-Hellman realization of the adapted protocol is provided. This paper also presents a genuine with dummy approach to achieving asymmetrical federated model training. To illustrate its application, a federated logistic regression algorithm is provided as an example. Experiments are also made for validating the feasibility of this approach.