mlsanityData to 7 Oct 2026

Vertical Federated Learning: Taxonomies, Threats, and Prospects

arXiv 2302.01550 ↗First posted 3 Feb 2023cs.LGcited by 8

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Abstract (arXiv)

Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for processing. Instead, local models are trained with local data; the models are then shared and combined. This approach preserves data privacy as locally trained models are shared instead of the raw data themselves. Broadly, FL can be divided into horizontal federated learning (HFL) and vertical federated learning (VFL). For the former, different parties hold different samples over the same set of features; for the latter, different parties hold different feature data belonging to the same set of samples. In a number of practical scenarios, VFL is more relevant than HFL as different companies (e.g., bank and retailer) hold different features (e.g., credit history and shopping history) for the same set of customers. Although VFL is an emerging area of research, it is not well-established compared to HFL. Besides, VFL-related studies are dispersed, and their connections are not intuitive. Thus, this survey aims to bring these VFL-related studies to one place. Firstly, we classify existing VFL structures and algorithms. Secondly, we present the threats from security and privacy perspectives to VFL. Thirdly, for the benefit of future researchers, we discussed the challenges and prospects of VFL in detail.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 47 of its 97 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

47 of 97 references.

References · 47

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35 more
  1. 2020Algorithmic Fairnesscited by 143
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  3. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
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  18. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 26
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  22. 2022Flexible Vertical Federated Learning with Heterogeneous Partiescited by 20
  23. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  24. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
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  30. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 1.

8 papers.

  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  3. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  4. 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systemscited by 1
  5. 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learningcited by 0
  6. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  7. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
  8. 2026CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learningcited by 0