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.
Available: https://www.ndss-symposium.org/ndss-paper/ fltrust-byzantine-robust-federated-learning-via-trust-bootstrapping/
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.