mlsanityData to 7 Oct 2026

Poisoning Attacks and Defenses in Federated Learning: A Survey

arXiv 2301.05795 ↗First posted 14 Jan 2023cs.CR cs.LGcited by 3

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Abstract (arXiv)

Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 15 references that are in the map. First posted on arXiv 14 Jan 2023.

Builds on

13 of 15 references.

References · 13

  1. 2018How To Backdoor Federated Learningcited by 745
  2. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  3. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  4. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  5. 2017Certified Defenses for Data Poisoning Attackscited by 288
  6. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  7. 2019Can You Really Backdoor Federated Learning?cited by 250
  8. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  9. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  10. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  11. 2020BaFFLe: Backdoor detection via Feedback-based Federated Learningcited by 52
  12. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 29
1 more
  1. 2021FLAME: Taming Backdoors in Federated Learning (Extended Version 1)cited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  2. 2024Fed-Credit: Robust Federated Learning with Credibility Managementcited by 0
  3. 2026PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learningcited by 0