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FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning
Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing tampered updates. Existing defenses often rely on impractical assumptions, such as access to a central test dataset, or fail to generalize across diverse
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