# Multi-Participant Multi-Class Vertical Federated Learning

arXiv: https://arxiv.org/abs/2001.11154 · HTML: https://mlsanity.com/paper/2001.11154/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 30 of 47)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://mlsanity.com/paper/2104.00489/) (2021)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics](https://mlsanity.com/paper/2401.05146/) (2024)
- [Practical Vertical Federated Learning with Unsupervised Representation Learning](https://mlsanity.com/paper/2208.10278/) (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)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2305.02219/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [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)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)
- [Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM](https://mlsanity.com/paper/2207.10226/) (2022)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [VFLAIR: A Research Library and Benchmark for Vertical Federated Learning](https://mlsanity.com/paper/2310.09827/) (2023)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Multi-Tier Federated Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2102.03620/) (2021)
- [Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond](https://mlsanity.com/paper/2207.09087/) (2022)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning](https://mlsanity.com/paper/2108.08930/) (2021)

## Abstract (arXiv)

Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL problems involving multiple parties. Extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacypreserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. Experiment results on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match multi-class classification performance of existing approaches.