SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, conventional FL approaches often struggle to deliver accurate and personalized models in the presence of non-IID data. Although model pruning has been proposed to improve model adaptability, existing methods relying solely on local data often yield sub-optimal sub-models due to limited task-specific information. To address this, we propose SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalization by integrating client clustering with Similar Client Structure Information (SCSI)-guided pruning. SAFL adopts a two-stage process: it first clusters clients based on data similarity and uses aggregated structural insights to guide pruning; then, clients train the resulting sub-models and participate in heterogeneous model aggregation. Extensive experiments on benchmark datasets demonstrate that SAFL achieves superior accuracy and model compactness compared to existing methods, particularly under non-IID settings. These results highlight the effectiveness of structure-aware pruning and collaboration in advancing personalized federated learning.