FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning
FedNIA injects random noise inputs to analyze the layerwise activation patterns in client models leveraging an autoencoder that detects abnormal behaviors indicative of data poisoning and can defend against diverse attack types, including sample poisoning, label flipping, and backdoors.