Junlin Che

Clustered federated learning has garnered significant attention as an effective strategy for enhancing model performance in non-independent and identically distributed (non-IID) data scenarios. This approach improves model performance in such environments by calculating the similarity between users and clustering them into multiple groups. However, several challenges arise when implementing this method, particularly in balancing flexibility, communication costs, and model performance. To address these issues, this paper proposes a novel hierarchical federated learning framework that balances both network and model performance. The framework performs principal component analysis (PCA) on device-side image datasets to assess the similarity of private data across devices and, in conjunction with network performance measurements, dynamically adjusts communication strategies to minimize latency while ensuring stable model performance. By weighting similarity and communication metrics, the framework optimizes communication efficiency without significantly compromising model performance. To validate the proposed method’s effectiveness, we employed three publicly available datasets and compared it against four baseline methods. The experimental results demonstrate that SC-Fed (segmented clustering-federated learning) achieves a maximum accuracy improvement of 7.56% over baseline methods, while also reducing the average waiting time by 54.6%. These results indicate that the proposed algorithm significantly enhances the applicability and efficiency of clustered federated learning in practical training scenarios.