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FedBBA slashes backdoor attack success rates to as low as 1.1% in federated learning, leaving existing defenses in the dust.
Backdoor defenses can be baked into the pre-training phase of federated learning, achieving state-of-the-art attack mitigation with minimal impact on clean accuracy.
