Frequency-Domain Signatures for Proactive Defense Against Model Poisoning Attacks in Federated Learning

Summary

This paper proposes SpecShield, a defense against model poisoning in federated learning that actively probes each client model with calibrated adversarial perturbations generated by the Fast Gradient Sign Method on the server. The resulting client responses are analyzed in the frequency domain with the Discrete Wavelet Transform to separate benign from malicious clients, and the authors report consistently higher detection accuracy and robustness than existing defenses across six state-of-the-art poisoning attacks on real-world datasets.

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