PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning
Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is widely applied in industrial image classification. However, due to its distributed nature, FL is vulnerable to threats from malicious clients, with poisoning attacks being a common threat. A major limitation of existing poisoning attack methods is their difficulty in bypassing model performance tests and defense mechanisms based on model anomaly detection. This often results in the detection and removal of poisoned models, which undermines their practical utility. To ensure both the performance of industrial image ...
| Authors | Tao Liu, Jiguang Lv, Dapeng Man et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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