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

Federated Learning enables decentralized model training without exposing raw data, but remains fundamentally vulnerable to poisoning attacks from malicious clients. Existing defenses rely heavily on passive anomaly detection, honest majority assumptions, or unrealistic statistical priors, making them ineffective against adaptive and stealthy adversaries. In this paper, we propose SpecShield, a proactive defense mechanism that actively probes client models through calibrated adversarial perturbations. By leveraging the Fast Gradient Sign Method on the server side, SpecShield elicits dynamic response patterns from each client. These responses are then analyzed in the frequency domain using the Discrete Wavelet Transform. These frequency-domain features uncover distinctive response patterns between benign and malicious clients, enabling robust detection of model poisoning attacks in both non-IID environments and Byzantine majority scenarios. We further derive theoretical upper bounds on perturbation magnitudes to guarantee detection accuracy while preserving benign client performance. Through extensive experiments conducted on real-world datasets under six state-of-the-art poisoning attacks, SpecShield consistently outperforms existing defenses in both detection accuracy and model robustness. Our results demonstrate that active perturbation-induced profiling provides a new dimension for securing federated learning against sophisticated adversarial threats.