Classifier Exchanging

Classifier exchanging is a technique in machine learning focused on improving the performance and efficiency of student models by leveraging knowledge from teacher models. Current research emphasizes methods like knowledge distillation, where a shared classifier between teacher and student networks enhances knowledge transfer, particularly addressing challenges in federated learning with heterogeneous data. This approach shows promise in improving model accuracy and reducing computational costs across various tasks, including image classification and face verification, by facilitating more effective information exchange between models. The resulting improvements in model training efficiency and performance have significant implications for resource-constrained applications and large-scale distributed learning.