mlsanityData to 14 Oct 2026

Sybil-based Virtual Data Poisoning Attacks in Federated Learning

arXiv 2505.09983 ↗First posted 15 May 2025cs.CR cs.LGcited by 0

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

Abstract (arXiv)

Federated learning is vulnerable to poisoning attacks by malicious adversaries. Existing methods often involve high costs to achieve effective attacks. To address this challenge, we propose a sybil-based virtual data poisoning attack, where a malicious client generates sybil nodes to amplify the poisoning model's impact. To reduce neural network computational complexity, we develop a virtual data generation method based on gradient matching. We also design three schemes for target model acquisition, applicable to online local, online global, and offline scenarios. In simulation, our method outperforms other attack algorithms since our method can obtain a global target model under non-independent uniformly distributed data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 16 of its 31 references that are in the map. First posted on arXiv 15 May 2025.

Builds on

16 of 31 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  7. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  8. 2017Deep & Cross Network for Ad Click Predictionscited by 393
  9. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  10. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  11. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  12. 2020Witches' Brew: Industrial Scale Data Poisoning via Gradient Matchingcited by 163
4 more
  1. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  2. 2021Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Drivingcited by 36
  3. 2022HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillationcited by 11
  4. 2022Global Update Guided Federated Learningcited by 2

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  1. 2024Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
  2. 2025Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
  3. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  4. 2022FedCC: Robust Federated Learning against Model Poisoning Attacks
  5. 2024Data Poisoning and Leakage Analysis in Federated Learning
  6. 2022An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
  7. 2024Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
  8. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning