mlsanityData to 7 Oct 2026

Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly

arXiv 2502.08160 ↗First posted 12 Feb 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL systems in real-world applications remains limited. To investigate the gap between existing VFL research and practical deployment, this survey analyzes the real-world data distributions in potential VFL applications and identifies four key findings that highlight this gap. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Our comprehensive review of existing VFL algorithms reveals that some common practical VFL scenarios have few or no viable solutions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 43 of its 71 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

43 of 71 references.

References · 43

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,338
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  5. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  6. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  7. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  8. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  9. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  10. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 66
  11. 2023Self-Supervised Multimodal Learning: A Surveycited by 58
  12. 2023Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytescited by 55
31 more
  1. 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
  2. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  3. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 35
  4. 2020When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Controlcited by 32
  5. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  6. 2024Membership Inference Attacks and Defenses in Federated Learning: A Surveycited by 27
  7. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  8. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  9. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  10. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  11. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  12. 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
  13. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  14. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  15. 2023BadVFL: Backdoor Attacks in Vertical Federated Learningcited by 9
  16. 2021GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborationscited by 8
  17. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  18. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  19. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  20. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  21. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaborationcited by 6
  22. 2023FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learningcited by 6
  23. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  24. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  25. 2023Input Reconstruction Attack against Vertical Federated Large Language Modelscited by 5
  26. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  27. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  28. 2022Hijack Vertical Federated Learning Models As One Partycited by 4
  29. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  30. 2024OFL-W3: A One-shot Federated Learning System on Web 3.0cited by 3
  31. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 1
  2. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  3. 2026Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inferencecited by 0
  4. 2026A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regressioncited by 0