mlsanityData to 7 Oct 2026

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

arXiv 2307.02040 ↗First posted 5 Jul 2023cs.LG cs.AIcited by 6

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Abstract (arXiv)

Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmarks often resort to synthetic datasets, derived from arbitrary feature splits from a global set, which only capture a subset of feature distributions, leading to inadequate algorithm performance assessment. This paper addresses these shortcomings by introducing two key factors affecting VFL performance - feature importance and feature correlation - and proposing associated evaluation metrics and dataset splitting methods. Additionally, we introduce a real VFL dataset to address the deficit in image-image VFL scenarios. Our comprehensive evaluation of cutting-edge VFL algorithms provides valuable insights for future research in the field.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 76 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

40 of 76 references.

References · 40

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28 more
  1. 2019The Shapley Taylor Interaction Indexcited by 143
  2. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
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  4. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  5. 2019Measure Contribution of Participants in Federated Learningcited by 78
  6. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  3. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  4. 2025ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learningcited by 0
  5. 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Siloscited by 0
  6. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0