SecureFed combines PCA-based anomaly detection, validation loss, and gradient magnitude into a trust score that controls zone-weighted aggregation, improving accuracy over vanilla FedAvg in a small MNIST label-flipping experiment.
Advances and open problems in federated learning,
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SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
SecureFed combines PCA-based anomaly detection, validation loss, and gradient magnitude into a trust score that controls zone-weighted aggregation, improving accuracy over vanilla FedAvg in a small MNIST label-flipping experiment.