mlsanityData to 14 Oct 2026

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning

arXiv 2507.00423 ↗First posted 1 Jul 2025cs.CR cs.DC cs.LGcited by 1

On the map: LLM safety and security → Federated unlearning and poisoning

Abstract (arXiv)

Federated learning (FL) allows multiple clients to collaboratively train a global machine learning model with coordination from a central server, without needing to share their raw data. This approach is particularly appealing in the era of privacy regulations like the GDPR, leading many prominent companies to adopt it. However, FL's distributed nature makes it susceptible to poisoning attacks, where malicious clients, controlled by an attacker, send harmful data to compromise the model. Most existing poisoning attacks in FL aim to degrade the model's integrity, such as reducing its accuracy, with limited attention to privacy concerns from these attacks. In this study, we introduce FedPoisonMIA, a novel poisoning membership inference attack targeting FL. FedPoisonMIA involves malicious clients crafting local model updates to infer membership information. Additionally, we propose a robust defense mechanism to mitigate the impact of FedPoisonMIA attacks. Extensive experiments across various datasets demonstrate the attack's effectiveness, while our defense approach reduces its impact to a degree.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 38 of its 58 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

38 of 58 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Poisoning Attacks and Defenses to Federated Unlearning29 Jan

Other references · 37

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2021Membership Inference Attacks From First Principlescited by 878
  7. 2018How To Backdoor Federated Learningcited by 765
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  9. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  10. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  11. 2020Label-Only Membership Inference Attackscited by 388
  12. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
25 more
  1. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  2. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  3. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 302
  4. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  5. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  6. 2019Can You Really Backdoor Federated Learning?cited by 255
  7. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  8. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 248
  9. 2017Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimizationcited by 197
  10. 2019Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inferencecited by 175
  11. 2019Privacy Risks of Securing Machine Learning Models against Adversarial Examplescited by 141
  12. 2018Generalized Byzantine-tolerant SGDcited by 95
  13. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 81
  14. 2021Property Inference From Poisoningcited by 60
  15. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  16. 2018Towards Demystifying Membership Inference Attackscited by 40
  17. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  18. 2024Poisoning Federated Recommender Systems with Fake Userscited by 15
  19. 2025Do We Really Need to Design New Byzantine-robust Aggregation Rules?cited by 15
  20. 2018Phocas: dimensional Byzantine-resilient stochastic gradient descentcited by 13
  21. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  22. 2024Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attackscited by 11
  23. 2024Poisoning Attacks on Federated Learning-based Wireless Traffic Predictioncited by 10
  24. 2025Provably Robust Federated Reinforcement Learningcited by 8
  25. 2025Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computingcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026United We Defend: Collaborative Membership Inference Defenses in Federated Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  2. 2024Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
  3. 2024Membership Inference Attacks and Defenses in Federated Learning: A Survey
  4. 2024Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
  5. 2024Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
  6. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
  7. 2024FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
  8. 2023FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks