mlsanityData to 14 Oct 2026

Vertical Federated Learning: Concepts, Advances and Challenges

arXiv 2211.12814 ↗First posted 23 Nov 2022cs.LG cs.AI cs.CRcited by 108

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Abstract (arXiv)

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL.

Key facts. Cited by 108 papers in the map (data through 14 Oct 2026). Builds on 95 of its 207 references that are in the map. First posted on arXiv 23 Nov 2022.

Builds on

95 of 207 references.

References · 95

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  5. 2019Deep Leakage from Gradientscited by 1,021
  6. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  7. 2021Towards Personalized Federated Learningcited by 464
  8. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  9. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  10. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  11. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  12. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
38 more
  1. 2021Federated Learning for Open Bankingcited by 141
  2. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  3. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  4. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
  5. 2017Unsupervised Learning by Predicting Noisecited by 98
  6. 2021Label Leakage and Protection in Two-party Split Learningcited by 90
  7. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  8. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  9. 2018Byzantine Stochastic Gradient Descentcited by 81
  10. 2019Measure Contribution of Participants in Federated Learningcited by 79
  11. 2019Overlearning Reveals Sensitive Attributescited by 76
  12. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  13. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  14. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  15. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  16. 2018AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learningcited by 60
  17. 2020Federated Bandit: A Gossiping Approachcited by 55
  18. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
  19. 2019Federated Forestcited by 49
  20. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  21. 2020SplitNN-driven Vertical Partitioningcited by 42
  22. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  23. 2020Federated Transfer Learning for EEG Signal Classificationcited by 37
  24. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  25. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 36
  26. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 36
  27. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  28. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  29. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  30. 2019Interpret Federated Learning with Shapley Valuescited by 33
  31. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  32. 2020When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Controlcited by 32
  33. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  34. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  35. 2020Backdoor attacks and defenses in feature-partitioned collaborative learningcited by 30
  36. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  37. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  38. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 27

50 most cited of 95 All 95 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 19; 2024: 30; 2025: 37; 2026: 21.

108 papers.

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  1. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  2. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  3. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  4. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  5. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  6. 2023A Simple Data Augmentation for Feature Distribution Skewed Federated Learningcited by 17
  7. 2023FedHCA2: Towards Hetero-Client Federated Multi-Task Learningcited by 17
  8. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  9. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  10. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 10
  11. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
  12. 2024FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfercited by 9
38 more
  1. 2025Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attackscited by 9
  2. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  5. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  6. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  7. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaborationcited by 6
  8. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  9. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  10. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  11. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  12. 2024Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosurecited by 5
  13. 2025Multiplayer Federated Learning: Reaching Equilibrium with Less Communicationcited by 5
  14. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  15. 2025FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learningcited by 5
  16. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  17. 2025Towards Harnessing the Collaborative Power of Large and Small Models for Domain Taskscited by 4
  18. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 3
  19. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  20. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  21. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  22. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
  23. 2025Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learningcited by 3
  24. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  25. 2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
  26. 2025From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learningcited by 3
  27. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  28. 2023Privacy-preserving design of graph neural networks with applications to vertical federated learningcited by 2
  29. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  30. 2024Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approachcited by 2
  31. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  32. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  33. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  34. 2025SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learningcited by 2
  35. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  36. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  37. 2026Collaborative Adaptive Curriculum for Progressive Knowledge Distillationcited by 2
  38. 2026FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphscited by 2

50 most cited of 108 All 108 (JSON)