mlsanityData to 14 Oct 2026

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

arXiv 2310.09827 ↗First posted 15 Oct 2023cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. VFL has gained significant attention for its research potential and real-world applications in recent years, but still faces substantial challenges, such as in defending various kinds of data inference and backdoor attacks. Moreover, most of existing VFL projects are industry-facing and not easily used for keeping track of the current research progress. To address this need, we present an extensible and lightweight VFL framework VFLAIR (available at https://github.com/FLAIR-THU/VFLAIR), which supports VFL training with a variety of models, datasets and protocols, along with standardized modules for comprehensive evaluations of attacks and defense strategies. We also benchmark 11 attacks and 8 defenses performance under different communication and model partition settings and draw concrete insights and recommendations on the choice of defense strategies for different practical VFL deployment scenarios.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 48 of its 91 references that are in the map. First posted on arXiv 15 Oct 2023.

Builds on

48 of 91 references.

References · 48

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Wide & Deep Learning for Recommender Systemscited by 747
  3. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  4. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  5. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  6. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  7. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  8. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  9. 2021Deep Learning with Label Differential Privacycited by 117
  10. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  11. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
  12. 2018A generic framework for privacy preserving deep learningcited by 103
36 more
  1. 2021Label Leakage and Protection in Two-party Split Learningcited by 90
  2. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  3. 2019Overlearning Reveals Sensitive Attributescited by 76
  4. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  5. 2019Federated Forestcited by 49
  6. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  7. 2018Product-based Neural Networks for User Response Prediction over Multi-field Categorical Datacited by 44
  8. 2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36
  9. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  10. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  11. 2020Backdoor attacks and defenses in feature-partitioned collaborative learningcited by 30
  12. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  13. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 27
  14. 2020Large-Scale Secure XGB for Vertical Federated Learningcited by 26
  15. 2022Differentially Private Label Protection in Split Learningcited by 26
  16. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  17. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
  18. 2022Flexible Vertical Federated Learning with Heterogeneous Partiescited by 20
  19. 2019SecureGBM: Secure Multi-Party Gradient Boostingcited by 19
  20. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  21. 2022FedAudio: A Federated Learning Benchmark for Audio Taskscited by 16
  22. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  23. 2018Secure Federated Transfer Learningcited by 13
  24. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  25. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  26. 2022A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learningcited by 10
  27. 2021An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimizationcited by 9
  28. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 9
  29. 2021Achieving Model Fairness in Vertical Federated Learningcited by 8
  30. 2020Mitigating Leakage in Federated Learning with Trusted Hardwarecited by 6
  31. 2022An Efficient and Robust System for Vertically Federated Random Forestcited by 6
  32. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  33. 2022FedGBF: An efficient vertical federated learning framework via gradient boosting and baggingcited by 5
  34. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  35. 2023Mutual Information Regularization for Vertical Federated Learningcited by 4
  36. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 3.

8 papers.

  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2025Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learningcited by 3
  3. 2026Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitablecited by 1
  4. 2023EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learningcited by 0
  5. 2023AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learningcited by 0
  6. 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Siloscited by 0
  7. 2026LEA: Label Enumeration Attack in Vertical Federated Learningcited by 0
  8. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
  2. 2023BadVFL: Backdoor Attacks in Vertical Federated Learning
  3. 2026Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
  4. 2024Vertical Federated Unlearning via Backdoor Certification
  5. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
  6. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  7. 2023Practical and General Backdoor Attacks against Vertical Federated Learning
  8. 2023FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection