Aash Mohammad developed privacy-preserving machine learning capabilities for the APPFL/APPFL repository, focusing on integrating differential privacy into federated learning workflows. He implemented Opacus-based training, enabling configurable privacy options and Gaussian mechanisms within the training pipeline. Using Python and PyTorch, Aash updated the ResNet model to ensure compatibility with Opacus and extended the VanillaTrainer to support differential privacy workflows. He also revised the serial federated learning notebook to demonstrate privacy-preserving experiments. The work demonstrated a deep understanding of configuration management and privacy techniques, resulting in a robust, configurable foundation for secure federated learning without addressing bug fixes during this period.