 PrivateKT is a novel knowledge transfer method that allows for efficient and secure knowledge transfer in federated learning. It utilizes a small public dataset to maximize the performance of federated learning while maintaining privacy. The method was tested on three different datasets and showed significant improvements over existing federated learning methods. This suggests that PrivateKT could provide a viable solution for knowledge transfer in machine intelligence systems. This article was authored by Daoqi, Fangjao Wu, Chuhan Wu, and others.