GradPFL: Gradient-Driven Adaptive Clustering in Personalized Federated Learning

Abstract

Many existing personalized federated learning (PFL) methods utilize clustering-based aggregation to group clients with similar data characteristics, improving model performance by promoting collaboration among clients with shared features. While this method effectively mitigates some challenges posed by data heterogeneity, it predominantly relies on static data features, making it challenging to capture the dynamic changes in client models during iterative training. This limitation impedes accurate clustering based on evolving model updates. To address this issue, we propose a Gradient-Driven Adaptive Clustering method in PFL (GradPFL), which more effectively captures the personalized deviations in locally updated models. Our approach also introduces an adaptive historical gradient mechanism that refines the clustering process by incorporating both current and past update characteristics. This enables more accurate model aggregation that adapts to ongoing changes in client models during training. Experimental results demonstrate that GradPFL outperforms existing clustering-based PFL methods, especially in more complex non-IID environments.