mlsanityData to 7 Oct 2026

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning

arXiv 2410.02042 ↗First posted 2 Oct 2024cs.LG cs.AI cs.CRcited by 4

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Abstract (arXiv)

Federated Learning (FL) is a technique that allows multiple parties to train a shared model collaboratively without disclosing their private data. It has become increasingly popular due to its distinct privacy advantages. However, FL models can suffer from biases against certain demographic groups (e.g., racial and gender groups) due to the heterogeneity of data and party selection. Researchers have proposed various strategies for characterizing the group fairness of FL algorithms to address this issue. However, the effectiveness of these strategies in the face of deliberate adversarial attacks has not been fully explored. Although existing studies have revealed various threats (e.g., model poisoning attacks) against FL systems caused by malicious participants, their primary aim is to decrease model accuracy, while the potential of leveraging poisonous model updates to exacerbate model unfairness remains unexplored. In this paper, we propose a new type of model poisoning attack, EAB-FL, with a focus on exacerbating group unfairness while maintaining a good level of model utility. Extensive experiments on three datasets demonstrate the effectiveness and efficiency of our attack, even with state-of-the-art fairness optimization algorithms and secure aggregation rules employed.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

36 of 55 references.

References · 36

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24 more
  1. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  2. 2019Robust Aggregation for Federated Learningcited by 314
  3. 2019Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgettingcited by 302
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  5. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  6. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
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  13. 2020Exacerbating Algorithmic Bias through Fairness Attackscited by 47
  14. 2021SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsificationcited by 46
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  18. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 29
  19. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
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  21. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
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  23. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  24. 2021DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learningcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  2. 2025Fairness-Constrained Optimization Attack in Federated Learningcited by 1
  3. 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspectivecited by 0
  4. 2026Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learningcited by 0