EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typically rely on fixed thresholds or perform clustering with a fixed number of clusters to distinguish malicious gradients from benign ones. However, these methods are difficult to adapt to dynamic poisoning strategies of malicious clients, and often result in the loss of benign gradients due to the heterogeneity of clients' local datasets. To address these problems, we propose a novel robust aggregation method that leverages a small number of known benign clients as references, enabling accurate identification and filtering of malicious gradi...
| Authors | Tianyun Zhang, Zhen Yang, Haozhao Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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