mlsanityData to 14 Oct 2026

VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification

arXiv 2408.15591 ↗First posted 28 Aug 2024cs.LGcited by 4

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Abstract (arXiv)

Vertical Federated Learning (VFL) focuses on handling vertically partitioned data over FL participants. Recent studies have discovered a significant vulnerability in VFL to backdoor attacks which specifically target the distinct characteristics of VFL. Therefore, these attacks may neutralize existing defense mechanisms designed primarily for Horizontal Federated Learning (HFL) and deep neural networks. In this paper, we present the first backdoor defense, called VFLIP, specialized for VFL. VFLIP employs the identification and purification techniques that operate at the inference stage, consequently improving the robustness against backdoor attacks to a great extent. VFLIP first identifies backdoor-triggered embeddings by adopting a participant-wise anomaly detection approach. Subsequently, VFLIP conducts purification which removes the embeddings identified as malicious and reconstructs all the embeddings based on the remaining embeddings. We conduct extensive experiments on CIFAR10, CINIC10, Imagenette, NUS-WIDE, and BankMarketing to demonstrate that VFLIP can effectively mitigate backdoor attacks in VFL. https://github.com/blingcho/VFLIP-esorics24

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

20 of 34 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  6. 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 580
  7. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  8. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  9. 2021Anti-Backdoor Learning: Training Clean Models on Poisoned Datacited by 263
  10. 2021Adversarial Neuron Pruning Purifies Backdoored Deep Modelscited by 235
  11. 2020fastai: A Layered API for Deep Learningcited by 183
  12. 2020Threats to Federated Learning: A Surveycited by 178
8 more
  1. 2020MetaPoison: Practical General-purpose Clean-label Data Poisoningcited by 120
  2. 2021Backdoor Attack in the Physical Worldcited by 84
  3. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  4. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  5. 2023Backdoor Defense via Adaptively Splitting Poisoned Datasetcited by 43
  6. 2023Towards Stable Backdoor Purification through Feature Shift Tuningcited by 32
  7. 2023Practical and General Backdoor Attacks against Vertical Federated Learningcited by 6
  8. 2022Local Contrastive Feature learning for Tabular Datacited by 4

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Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2025On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attackcited by 0
  2. 2025HASSLE: A Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learningcited by 0
  3. 2026SecureSplit: Mitigating Backdoor Attacks in Split Learningcited by 0
  4. 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. 2023BadVFL: Backdoor Attacks in Vertical Federated Learning
  2. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
  3. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
  4. 2026Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
  5. 2023FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
  6. 2023On the Vulnerability of Backdoor Defenses for Federated Learning
  7. 2023Mutual Information Regularization for Vertical Federated Learning
  8. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration