mlsanityData to 14 Oct 2026

Learning to Backdoor Federated Learning

arXiv 2303.03320 ↗First posted 6 Mar 2023cs.LG cs.CR cs.DCcited by 8

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Abstract (arXiv)

In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this end, various defenses, including training stage aggregation-based defenses and post-training mitigation defenses, have been proposed recently. While these defenses obtain reasonable performance against existing backdoor attacks, which are mainly heuristics based, we show that they are insufficient in the face of more advanced attacks. In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training. Our attack framework is both adaptive and flexible and achieves strong attack performance and durability even under state-of-the-art defenses.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

12 of 19 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2016OpenAI Gymcited by 1,736
  7. 2018How To Backdoor Federated Learningcited by 765
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  9. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  10. 2019Can You Really Backdoor Federated Learning?cited by 255
  11. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 81
  12. 2021FLAME: Taming Backdoors in Federated Learning (Extended Version 1)cited by 16

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Cited by year

Citing papers per year: 2023: 5; 2024: 2; 2025: 1.

8 papers.

  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  2. 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivitycited by 11
  3. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  4. 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 9
  5. 2023A First Order Meta Stackelberg Method for Robust Federated Learningcited by 7
  6. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  7. 2023Towards Understanding Adversarial Transferability in Federated Learningcited by 1
  8. 2025POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023On the Vulnerability of Backdoor Defenses for Federated Learning
  2. 2024Client-Side Patching against Backdoor Attacks in Federated Learning
  3. 2022Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
  4. 2025Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
  5. 2025Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction
  6. 2023FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
  7. 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
  8. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions