Global Distillation

Global distillation in machine learning focuses on transferring knowledge from a larger, more complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance and reducing computational demands. Current research emphasizes techniques like feature distillation, which focuses on aligning feature representations between teacher and student, and logit distillation, which targets the output probabilities. These methods are applied across various tasks, including federated learning (where data is distributed across multiple devices), object detection in images, and sparse-view computed tomography reconstruction, improving model accuracy and efficiency in resource-constrained environments. The resulting advancements have significant implications for deploying complex models on edge devices and enhancing the scalability and robustness of machine learning systems.