This paper introduces FL-PBM, a novel client-side pre-training defense mechanism for Federated Learning to mitigate backdoor attacks. It proactively identifies and neutralizes poisoned data using PCA, GMM clustering, and adaptive blurring before local model training, significantly reducing attack success rates while maintaining high model accuracy.