AKPFL:A Personalized Federated Learning Architecture to Alleviate Statistical Heterogeneity

Federated Learning FL has gained considerable attention in machine learning for its ability to preserve data privacy while enabling collaborative modeling. However, statistical heterogeneity, such as non-independent and identically distributed non-IID data severely limits performance. To address this limitation, this study proposes an Adaptive Kernel Alignment-based Personalized Federated Learning framework AKPFL . This approach achieves a balance between global model sharing and local adaptation by incorporating a dynamic kernel adjustment mechanism and a personalized model fusion strategy, thereby improving model generalization and robustness in heterogeneous data environments. Experimental results demonstrate that, compared to existing algorithms, AKPFL delivers substantial improvements in test accuracy on datasets such as FashionMNIST, CIFAR-10, and CIFAR-100, particularly under high statistical heterogeneity. The code for the framework will be released publicly following the completion of the paper review process.