Federated Learning aims to enable joint training of high-performance deep learning by multiple clients while preserving data privacy by avoiding local data upload. The efficiency of collaborative learning is compromised by the heterogeneity of data distribution on different clients. In order to address this, we propose FedDCP, a generalized framework for personalized federated learning, that can mitigate the negative impact of long-tail distribution of local data. The core idea involves using the prototypes to limits the drift of local’s feature extraction and allowing personalized models to assimilate global insights alongside local data adaptation by dual classifier. Furthermore, a series of comprehensive experiments on three distinct datasets show that FedDCP can significantly enhance personalization capabilities of existing federated learning methods. In comparison to eight other SOTA pFL algorithms, the FedDCP demonstrates notable enhancements in accuracy. The code for FedDCP is publicly available on GitHub at https://github.com/awsl0/FedDCP .