mlsanityData to 7 Oct 2026

Membership Inference Attacks and Defenses in Federated Learning: A Survey

arXiv 2412.06157 ↗First posted 9 Dec 2024cs.CRcited by 27

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

Abstract (arXiv)

Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One of the key threats is membership inference attacks, which target clients' privacy by determining whether a specific example is part of the training set. These attacks can compromise sensitive information in real-world applications, such as medical diagnoses within a healthcare system. Although there has been extensive research on membership inference attacks, a comprehensive and up-to-date survey specifically focused on it within federated learning is still absent. To fill this gap, we categorize and summarize membership inference attacks and their corresponding defense strategies based on their characteristics in this setting. We introduce a unique taxonomy of existing attack research and provide a systematic overview of various countermeasures. For these studies, we thoroughly analyze the strengths and weaknesses of different approaches. Finally, we identify and discuss key future research directions for readers interested in advancing the field.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. First posted on arXiv 9 Dec 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 15; 2026: 12.

27 papers.

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  1. 2025OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Modelscited by 9
  2. 2025FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearningcited by 7
  3. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  4. 2025Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyondcited by 6
  5. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  6. 2025Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacycited by 4
  7. 2025SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controllingcited by 3
  8. 2026Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approachcited by 3
  9. 2025A Sample-Level Evaluation and Generative Framework for Model Inversion Attackscited by 2
  10. 2025Securing Genomic Data Against Inference Attacks in Federated Learning Environmentscited by 1
  11. 2025Onboard Optimization and Learning: A Surveycited by 1
  12. 2026SoK: Challenges in Tabular Membership Inference Attackscited by 1
15 more
  1. 2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluationcited by 1
  2. 2026Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMscited by 1
  3. 2025Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updatescited by 0
  4. 2025AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AIcited by 0
  5. 2025Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networkscited by 0
  6. 2025Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Responsecited by 0
  7. 2025ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Pushcited by 0
  8. 2026Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Imagescited by 0
  9. 2026Serverless AI Security: Attack Surface Analysis and Runtime Protection Mechanisms for FaaS-Based Machine Learningcited by 0
  10. 2026Fed-Listing: Federated Label Distribution Inference in Graph Neural Networkscited by 0
  11. 2026ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregationcited by 0
  12. 2026Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniquescited by 0
  13. 2026AI Security Research Should Better Incentivize Defense Researchcited by 0
  14. 2026Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approachcited by 0
  15. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0