mlsanityData to 14 Oct 2026

Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning

arXiv 2407.09958 ↗First posted 13 Jul 2024cs.CR cs.LGcited by 2

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Abstract (arXiv)

Federated Learning (FL) exposes vulnerabilities to targeted poisoning attacks that aim to cause misclassification specifically from the source class to the target class. However, using well-established defense frameworks, the poisoning impact of these attacks can be greatly mitigated. We introduce a generalized pre-training stage approach to Boost Targeted Poisoning Attacks against FL, called BoTPA. Its design rationale is to leverage the model update contributions of all data points, including ones outside of the source and target classes, to construct an Amplifier set, in which we falsify the data labels before the FL training process, as a means to boost attacks. We comprehensively evaluate the effectiveness and compatibility of BoTPA on various targeted poisoning attacks. Under data poisoning attacks, our evaluations reveal that BoTPA can achieve a median Relative Increase in Attack Success Rate (RI-ASR) between 15.3% and 36.9% across all possible source-target class combinations, with varying percentages of malicious clients, compared to its baseline. In the context of model poisoning, BoTPA attains RI-ASRs ranging from 13.3% to 94.7% in the presence of the Krum and Multi-Krum defenses, from 2.6% to 49.2% under the Median defense, and from 2.9% to 63.5% under the Flame defense.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 13 Jul 2024.

Builds on

39 of 63 references.

References · 39

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
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  12. 2019Contrastive Representation Distillationcited by 748
27 more
  1. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  4. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  5. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  6. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  7. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 446
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  12. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  13. 2017Certified Defenses for Data Poisoning Attackscited by 303
  14. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  15. 2019Can You Really Backdoor Federated Learning?cited by 255
  16. 2017Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimizationcited by 197
  17. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  18. 2021Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspectivecited by 119
  19. 2021Input Similarity from the Neural Network Perspectivecited by 79
  20. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 78
  21. 2020Subpopulation Data Poisoning Attackscited by 70
  22. 2022MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clientscited by 68
  23. 2020Understanding Adversarial Examples from the Mutual Influence of Images and Perturbationscited by 59
  24. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  25. 2023Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstructioncited by 21
  26. 2024BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learningcited by 3
  27. 2023Federated Deep Learning for Intrusion Detection in IoT Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Modelscited by 0
  2. 2026STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  2. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
  3. 2024Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
  4. 2022Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
  5. 2024Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
  6. 2024Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
  7. 2025Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
  8. 2024Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense