mlsanityData to 7 Oct 2026

Privacy Leakage of Adversarial Training Models in Federated Learning Systems

arXiv 2202.10546 ↗First posted 21 Feb 2022cs.LG cs.CR cs.CVcited by 7

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Abstract (arXiv)

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://github.com/zjysteven/PrivayAttack_AT_FL.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 24 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

21 of 24 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  8. 2016Wide Residual Networkscited by 3,057
  9. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  10. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  11. 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,337
  12. 2019Deep Leakage from Gradientscited by 1,004
9 more
  1. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  2. 2020RobustBench: a standardized adversarial robustness benchmarkcited by 659
  3. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  4. 2014Understanding Deep Image Representations by Inverting Themcited by 399
  5. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  6. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  7. 2020Do Adversarially Robust ImageNet Models Transfer Better?cited by 315
  8. 2019Privacy Risks of Securing Machine Learning Models against Adversarial Examplescited by 136
  9. 2019Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attackscited by 10

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Cited by year

Citing papers per year: 2023: 6; 2024: 1.

7 papers.

  1. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 178
  2. 2023Federated Learning for Computer Visioncited by 22
  3. 2023SoK: Unintended Interactions among Machine Learning Defenses and Riskscited by 6
  4. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  5. 2023Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environmentscited by 2
  6. 2023Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learningcited by 1
  7. 2023Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attackscited by 1