mlsanityData to 7 Oct 2026

Vertical Federated Learning: A Structured Literature Review

arXiv 2212.00622 ↗First posted 1 Dec 2022cs.LG cs.AI cs.DCcited by 17

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Abstract (arXiv)

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 59 of its 168 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

59 of 168 references.

References · 59

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  7. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  8. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  10. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  11. 2021A survey on datasets for fairness-aware machine learningcited by 157
  12. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
38 more
  1. 2021Deep Learning with Label Differential Privacycited by 113
  2. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  4. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  5. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  6. 2019Measure Contribution of Participants in Federated Learningcited by 78
  7. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  8. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  9. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  10. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  11. 2019Federated Forestcited by 49
  12. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  13. 2020Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentationcited by 45
  14. 2020FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learningcited by 44
  15. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  16. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 37
  17. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  18. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  19. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  20. 2022Federated Learning under Distributed Concept Driftcited by 35
  21. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  22. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  23. 2020FedADC: Accelerated Federated Learning with Drift Controlcited by 26
  24. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 26
  25. 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
  26. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  27. 2020Hybrid Differentially Private Federated Learning on Vertically Partitioned Datacited by 22
  28. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
  29. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  30. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  31. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  32. 2021Collaboration Equilibrium in Federated Learningcited by 15
  33. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  34. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  35. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  36. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  37. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  38. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9

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Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 7.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  4. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  5. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  6. 2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
  7. 2026Networked Information Aggregation for Binary Classificationcited by 3
  8. 2024Federated learning in food researchcited by 1
  9. 2025VFL-RPS: Relevant Participant Selection in Vertical Federated Learningcited by 1
  10. 2026Federated Imputation under Heterogeneous Feature Spacescited by 1
  11. 2024P3LS: Partial Least Squares under Privacy Preservationcited by 0
  12. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
5 more
  1. 2026Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulationcited by 0
  2. 2026HybridFL: A Federated Learning Approach for Financial Crime Detectioncited by 0
  3. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0
  4. 2026Expected Gain-based Escalation in Vertical Federated Learningcited by 0
  5. 2026Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updatescited by 0