Personalized Federated Learning Based on Dynamic Clustering and Modular Combinatorial Strategy
This paper proposes a personalized federated learning method based on dynamic clustering to address the issue of heterogeneous data in Federated Learning.This method combines the optimization target vector with the agglomerative clustering algorithm,dynamically divides clients with significant data differences into different clusters while conserving computing resources.Furthermore,in consideration of the sustainability of training models,the paper further proposes a modular combinatorial strategy,where new clients only need to combine previously trained models to obtain an initial model suitable for local tasks.The client only needs to perform a small amount of training on this initial model to apply it to local tasks.On the Cafir-10 and Minst datasets,the model's accuracy is superior to that of locally retrained models.
国家科技期刊平台
NSTL
万方数据
