Xiaolong Yu

A federated learning-based dual-filtering solution to combat data poisoning attacks in medical image classification

FedGF, an end-to-end two-stage defense scheme that combines client-side Genetic Data Selection with server-side Federated Unlearning enhanced by LOF-based anomaly detection, is proposed that consistently improves robustness and convergence stability compared with conventional aggregation defenses.