  Although many achievements have been made since Google threw out the paradigm
of federated learning (FL), there still exists much room for researchers to
optimize its efficiency. In this paper, we propose a high efficient FL method
equipped with the double head design aiming for personalization optimization
over non-IID dataset, and the gradual model sharing design for communication
saving. Experimental results show that, our method has more stable accuracy
performance and better communication efficient across various data
distributions than other state of art methods (SOTAs), makes it more
industry-friendly.
