mlsanityData to 7 Oct 2026

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

arXiv 2106.05468 ↗First posted 10 Jun 2021cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

12 of 19 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  7. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  8. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  9. 2020SplitNN-driven Vertical Partitioningcited by 42
  10. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  11. 2019A Communication Efficient Collaborative Learning Framework for Distributed Featurescited by 21
  12. 2020Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learningcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

5 papers.

  1. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  2. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  3. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  4. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  5. 2026Secure and Privacy-Preserving Vertical Federated Learningcited by 0