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FedDAB outperforms traditional defenses by effectively filtering out malicious updates in Federated Learning, ensuring robust model integrity against backdoor attacks.
Overcome performance degradation in heterogeneous federated learning by contrasting current models with historical training states, leading to more stable and accurate global models.
