Federated learning is an important method to address two critical challenges in machine learning:data sharing and privacy protection.However,federated learning itself faces challenges related to data heterogeneity and model heterogeneity.Existing researches often focus on addressing one of these issues while overlook the correlation between them.To address this,this paper introduces a framework named PFKD (Personalized Federated learning based on Knowledge Distillation).This framework utilizes knowledge distillation techniques to address model heterogeneity and personalized algorithms to tackle data heterogeneity,thereby achieving more personalized federated learning.Experimental analysis validates the effectiveness of the proposed framework.The experimental results demonstrate that the framework can overcome model performance bottlenecks and improve model accuracy by approximately one percentage point.Furthermore,with appropriate hyperparameter adjustment,the framework's performance is further enhanced.