DARCS, a reliability-based client selection and anomaly detection framework, keeps hierarchical federated learning in vehicular networks within 2-3% of attack-free accuracy and reduces convergence time under noise and gradient ascent attacks.
Poisoning attacks in federated learning: A survey,
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Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks
DARCS, a reliability-based client selection and anomaly detection framework, keeps hierarchical federated learning in vehicular networks within 2-3% of attack-free accuracy and reduces convergence time under noise and gradient ascent attacks.