mlsanityData to 7 Oct 2026

Vertical Federated Learning: Challenges, Methodologies and Experiments

arXiv 2202.04309 ↗First posted 9 Feb 2022cs.LG cs.DCcited by 55

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Abstract (arXiv)

Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, however with the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems under four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of our proposed solutions.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 9 Feb 2022.

Builds on

8 of 15 references.

References · 8

  1. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  2. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  3. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  4. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  5. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  6. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  7. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  8. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 14; 2024: 21; 2025: 10; 2026: 2.

55 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
  3. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  4. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  5. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  6. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  7. 2023Towards Quantum Federated Learningcited by 20
  8. 2022A Survey on Over-the-Air Computationcited by 19
  9. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  10. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  11. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  12. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
38 more
  1. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  2. 2024SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Modelscited by 10
  3. 2023BadVFL: Backdoor Attacks in Vertical Federated Learningcited by 9
  4. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  5. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  6. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  7. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  8. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  9. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  10. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  11. 2024A Systematic Review of Federated Generative Modelscited by 5
  12. 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
  13. 2023Efficient Vertical Federated Learning with Secure Aggregationcited by 3
  14. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  15. 2024VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purificationcited by 3
  16. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
  17. 2025Unlearning Clients, Features and Samples in Vertical Federated Learningcited by 3
  18. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  19. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 2
  20. 2024Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approachcited by 2
  21. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  22. 2024Scalable Vertical Federated Learning via Data Augmentation and Amortized Inferencecited by 2
  23. 2024Contrastive Federated Learning with Tabular Data Siloscited by 2
  24. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  25. 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoningcited by 1
  26. 2024Vertical Federated Learning Hybrid Local Pre-trainingcited by 1
  27. 2026SoK: Challenges in Tabular Membership Inference Attackscited by 1
  28. 2022Vertical Federated Linear Contextual Banditscited by 0
  29. 2024Vertical Federated Image Segmentationcited by 0
  30. 2024Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networkscited by 0
  31. 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learningcited by 0
  32. 2024Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoTcited by 0
  33. 2024ION-C: Integration of Overlapping Networks via Constraintscited by 0
  34. 2024Attributed Graph Clustering in Collaborative Settingscited by 0
  35. 2025Reliable Imputed-Sample Assisted Vertical Federated Learningcited by 0
  36. 2025Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networkscited by 0
  37. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  38. 2025Event-Driven Online Vertical Federated Learningcited by 0

50 most cited of 55 All 55 (JSON)