CSFL: Enhancing Splitfed Learning with Clustering on Non-IID Data

Distributed machine learning methods are gaining significant attention for their ability to enhance computational efficiency and safeguard privacy. Federated learning and split learning are two prominent approaches in this domain. Recently, splitfed learning, a hybrid of both methods, was introduced to address their individual limitations. However, splitfed learning overlooks the non-IID (non-Independent and Identically Distributed) problem commonly encountered in distributed environments, which can lead to substantial degradation in model performance. In this paper, we introduce Clustered Splitfed Learning (CSFL), a novel approach that integrates clustering with splitfed learning. We propose two training processes tailored to the degree of data heterogeneity: Non-Personalized Clustered Splitfed Learning (NPCSFL) and Personalized Clustered Splitfed Learning (PCSFL). Our experimental results demonstrate that CSFL significantly improves both model accuracy and convergence rates.