Differential Privacy & Federated Learning research focuses on enabling efficient machine learning model training and data analysis while protecting data privacy. This direction covers differential privacy mechanism design, federated learning algorithm optimization, privacy-preserving graph neural network training, and other key problems. It has important applications in healthcare, finance, scientific data sharing, and other domains