mlsanityData to 7 Oct 2026

Hijack Vertical Federated Learning Models As One Party

arXiv 2212.00322 ↗First posted 1 Dec 2022cs.LG cs.AI cs.CRcited by 4

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Abstract (arXiv)

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 46 of its 104 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

46 of 104 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  7. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  8. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  9. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  10. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
  11. 2016Universal adversarial perturbationscited by 774
  12. 2018How To Backdoor Federated Learningcited by 745
34 more
  1. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  2. 2019Adversarial Training for Free!cited by 638
  3. 2017One pixel attack for fooling deep neural networkscited by 579
  4. 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 550
  5. 2017Countering Adversarial Images using Input Transformationscited by 517
  6. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  7. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  8. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  9. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  10. 2019STRIP: A Defence Against Trojan Attacks on Deep Neural Networkscited by 437
  11. 2018TextBugger: Generating Adversarial Text Against Real-world Applicationscited by 376
  12. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  13. 2019Can You Really Backdoor Federated Learning?cited by 250
  14. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  15. 2017On the (Statistical) Detection of Adversarial Examplescited by 180
  16. 2016Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distributioncited by 158
  17. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  18. 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
  19. 2020Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Reviewcited by 120
  20. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  21. 2020An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networkscited by 92
  22. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 86
  23. 2020Subpopulation Data Poisoning Attackscited by 68
  24. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  25. 2019Adversarial Defense by Restricting the Hidden Space of Deep Neural Networkscited by 56
  26. 2020BaFFLe: Backdoor detection via Feedback-based Federated Learningcited by 52
  27. 2021One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objectivecited by 43
  28. 2018Targeted Adversarial Examples for Black Box Audio Systemscited by 41
  29. 2020Mitigating Backdoor Attacks in Federated Learningcited by 39
  30. 2019Interpret Federated Learning with Shapley Valuescited by 33
  31. 2020Backdoor attacks and defenses in feature-partitioned collaborative learningcited by 30
  32. 2019A Communication Efficient Collaborative Learning Framework for Distributed Featurescited by 21
  33. 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
  34. 2018Private Machine Learning in TensorFlow using Secure Computationcited by 15

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  3. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  4. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0