mlsanityData to 7 Oct 2026

FAT: Federated Adversarial Training

arXiv 2012.01791 ↗First posted 3 Dec 2020cs.LG cs.CRcited by 23

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Abstract (arXiv)

Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as the most promising approach against evasion threats on ML models. In this paper, we take the first known steps towards federated adversarial training (FAT) combining both methods to reduce the threat of evasion during inference while preserving the data privacy during training. We investigate the effectiveness of the FAT protocol for idealised federated settings using MNIST, Fashion-MNIST, and CIFAR10, and provide first insights on stabilising the training on the LEAF benchmark dataset which specifically emulates a federated learning environment. We identify challenges with this natural extension of adversarial training with regards to achieved adversarial robustness and further examine the idealised settings in the presence of clients undermining model convergence. We find that Trimmed Mean and Bulyan defences can be compromised and we were able to subvert Krum with a novel distillation based attack which presents an apparently "robust" model to the defender while in fact the model fails to provide robustness against simple attack modifications.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2020.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 5; 2025: 4; 2026: 1.

23 papers.

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  1. 2023Delving into the Adversarial Robustness of Federated Learningcited by 19
  2. 2021Adversarial training in communication constrained federated learningcited by 17
  3. 2022Federated Adversarial Learning: A Framework with Convergence Analysiscited by 17
  4. 2021Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learningcited by 9
  5. 2024Adversarial Training: A Surveycited by 6
  6. 2024Towards Robust Federated Learning via Logits Calibration on Non-IID Datacited by 5
  7. 2022Certified Robustness in Federated Learningcited by 4
  8. 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learningcited by 4
  9. 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
  10. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  11. 2022Characterizing Internal Evasion Attacks in Federated Learningcited by 1
  12. 2022Robust Learning Protocol for Federated Tumor Segmentation Challengecited by 1
11 more
  1. 2023Improving Machine Learning Robustness via Adversarial Trainingcited by 1
  2. 2023Towards Understanding Adversarial Transferability in Federated Learningcited by 1
  3. 2023Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learningcited by 1
  4. 2025Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligencecited by 1
  5. 2022Federated Adversarial Training with Transformerscited by 0
  6. 2022FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devicescited by 0
  7. 2024How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?cited by 0
  8. 2025Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustnesscited by 0
  9. 2025DART: A Server-side Plug-in for Resource-efficient Robust Federated Learningcited by 0
  10. 2025TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Trainingcited by 0
  11. 2026Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learningcited by 0