EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.
Shieldfl: Mitigating model poisoning attacks in privacy-preserving federated learning,
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EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.