mlsanityData to 7 Oct 2026

Delving into the Adversarial Robustness of Federated Learning

arXiv 2302.09479 ↗First posted 19 Feb 2023cs.LG cs.AI cs.CRcited by 19

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Abstract (arXiv)

In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largely unexplored. This paper casts light on the challenge of adversarial robustness of federated learning. To facilitate a better understanding of the adversarial vulnerability of the existing FL methods, we conduct comprehensive robustness evaluations on various attacks and adversarial training methods. Moreover, we reveal the negative impacts induced by directly adopting adversarial training in FL, which seriously hurts the test accuracy, especially in non-IID settings. In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems. Extensive experiments on multiple datasets demonstrate that DBFAT consistently outperforms other baselines under both IID and non-IID settings.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 37 of its 54 references that are in the map. First posted on arXiv 19 Feb 2023.

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37 of 54 references.

References · 37

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  8. 2016Adversarial examples in the physical worldcited by 1,762
  9. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  10. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
  11. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  12. 2018Federated Learning with Non-IID Datacited by 1,099
25 more
  1. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  2. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  4. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  5. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  6. 2021Anti-Backdoor Learning: Training Clean Models on Poisoned Datacited by 258
  7. 2018Adversarial Logit Pairingcited by 224
  8. 2020Geometry-aware Instance-reweighted Adversarial Trainingcited by 198
  9. 2018MMA Training: Direct Input Space Margin Maximization through Adversarial Trainingcited by 171
  10. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  11. 2022FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesiscited by 136
  12. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  13. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  14. 2020Learnable Boundary Guided Adversarial Trainingcited by 93
  15. 2020Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalizationcited by 73
  16. 2018Knowledge Distillation with Adversarial Samples Supporting Decision Boundarycited by 49
  17. 2022Towards Practical Certifiable Patch Defense with Vision Transformercited by 44
  18. 2021SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generationcited by 27
  19. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  20. 2020FAT: Federated Adversarial Trainingcited by 23
  21. 2022CalFAT: Calibrated Federated Adversarial Training with Label Skewnesscited by 20
  22. 2021Adversarial training in communication constrained federated learningcited by 17
  23. 2020Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learningcited by 13
  24. 2021Certifiably-Robust Federated Adversarial Learning via Randomized Smoothingcited by 9
  25. 2022A Secure and Efficient Federated Learning Framework for NLPcited by 9

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Citing papers per year: 2023: 4; 2024: 8; 2025: 7.

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  1. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  2. 2023Federated Incremental Semantic Segmentationcited by 36
  3. 2023Federated Generative Learning with Foundation Modelscited by 23
  4. 2024Adversarial Training: A Surveycited by 6
  5. 2024Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Trainingcited by 5
  6. 2024Towards Robust Federated Learning via Logits Calibration on Non-IID Datacited by 5
  7. 2025QFAL: Quantum Federated Adversarial Learningcited by 5
  8. 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
  9. 2025FedEAT: A Robustness Optimization Framework for Federated LLMscited by 3
  10. 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
  11. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  12. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
7 more
  1. 2024Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentationcited by 1
  2. 2025Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligencecited by 1
  3. 2025FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Modelscited by 1
  4. 2024Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shiftscited by 0
  5. 2024TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learningcited by 0
  6. 2025Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attackscited by 0
  7. 2025Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustnesscited by 0