mlsanityData to 7 Oct 2026

DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning

arXiv 2501.15005 ↗First posted 25 Jan 2025cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Distributed backdoor attacks (DBA) have shown a higher attack success rate than centralized attacks in centralized federated learning (FL). However, it has not been investigated in the decentralized FL. In this paper, we experimentally demonstrate that, while directly applying DBA to decentralized FL, the attack success rate depends on the distribution of attackers in the network architecture. Considering that the attackers can not decide their location, this paper aims to achieve a high attack success rate regardless of the attackers' location distribution. Specifically, we first design a method to detect the network by predicting the distance between any two attackers on the network. Then, based on the distance, we organize the attackers in different clusters. Lastly, we propose an algorithm to textitdynamically embed local patterns decomposed from a global pattern into the different attackers in each cluster. We conduct a thorough empirical investigation and find that our method can, in benchmark datasets, outperform both centralized attacks and naive DBA in different decentralized frameworks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

31 of 43 references.

References · 31

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018How To Backdoor Federated Learningcited by 745
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  5. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  6. 2019Fair Resource Allocation in Federated Learningcited by 389
  7. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  8. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  9. 2022A Survey of Machine Unlearningcited by 298
  10. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  11. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  12. 2019Federated Learning over Wireless Fading Channelscited by 178
19 more
  1. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 97
  2. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  3. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  4. 2019Practical Federated Gradient Boosting Decision Treescited by 53
  5. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  6. 2020Throughput-Optimal Topology Design for Cross-Silo Federated Learningcited by 37
  7. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  8. 2023Momentum Benefits Non-IID Federated Learning Simply and Provablycited by 37
  9. 2023One-shot Empirical Privacy Estimation for Federated Learningcited by 35
  10. 2023On the Vulnerability of Backdoor Defenses for Federated Learningcited by 27
  11. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  12. 2022SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communicationcited by 21
  13. 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learningcited by 20
  14. 2022Faster federated optimization under second-order similaritycited by 18
  15. 2023Backdoor Federated Learning by Poisoning Backdoor-Critical Layerscited by 16
  16. 2023Benchmarking Algorithms for Federated Domain Generalizationcited by 14
  17. 2023Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learningcited by 9
  18. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  19. 2023RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attackscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Optimized Local Updates in Federated Learning via Reinforcement Learningcited by 1