# Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners

arXiv: https://arxiv.org/abs/2106.05468 · HTML: https://mlsanity.com/paper/2106.05468/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SplitNN-driven Vertical Partitioning](https://mlsanity.com/paper/2008.04137/) (2020)
- [Entity Resolution and Federated Learning get a Federated Resolution](https://mlsanity.com/paper/1803.04035/) (2018)
- [A Communication Efficient Collaborative Learning Framework for Distributed Features](https://mlsanity.com/paper/1912.11187/) (2019)
- [Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning](https://mlsanity.com/paper/2011.09350/) (2020)

## Built on by (most cited first, 5 of 5)

- [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)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data](https://mlsanity.com/paper/2410.17986/) (2024)
- [Secure and Privacy-Preserving Vertical Federated Learning](https://mlsanity.com/paper/2604.13474/) (2026)

## Abstract (arXiv)

Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label information is owned by a single data owner. In this paper, we propose a novel method, Multi Vertical Federated Learning (Multi-VFL), to train VFL models when there are multiple data and label owners. Our approach is the first to consider the setting where D-data owners (across which features are distributed) and K-label owners (across which labels are distributed) exist. This proposed configuration allows different entities to train and learn optimal models without having to share their data. Our framework makes use of split learning and adaptive federated optimizers to solve this problem. For empirical evaluation, we run experiments on the MNIST and FashionMNIST datasets. Our results show that using adaptive optimizers for model aggregation fastens convergence and improves accuracy.