Our work, “SD-CSFL: A Synthetic Data-Driven Conformity Scoring Framework for Robust Federated Learning” was presented at WACV 2026. The work introduces a novel technique for robust federated learning, based on using synthetic data to defend against adversaries in a privacy-preserving way. Results show improved performance against both gradient manipulation and backdoors. Our paper is freely available, and our code is available on GitHub.