CLSM-FL: Clustering-Based Semantic Federated Learning in Non-IID IoT Environment

Federated learning (FL) stands as a robust framework facilitating collaborative learning in distributed Internet of Things (IoT) settings. Nevertheless, the proliferation of nonidentically and independently distributed (non-IID) data sets presents formidable challenges, revealing distinct attributes among local participants within the IoT network. In response, clustering has been incorporated into FL methodologies, bolstering adaptability to heterogeneous data set characteristics and ameliorating the effects of non-IID conditions. Despite its effectiveness, the interpretive aspects and ramifications of clustering within FL have received insufficient scrutiny, constraining its broader utility in artificial intelligence-infused IoT ecosystems. This study exposes a notable drawback in traditional clustering methods within FL, labeled as "Cluster Collapse Disorder," wherein cluster boundaries blur, leaving models susceptible to non-IID data and data drift. Inspired by this finding, we propose clustering-based semantic FL (CLSM-FL). Our framework employs a soft clustering technique based on the inherent attributes of local models, assigning them to semantic clusters for interpretable evaluation of their capabilities. Beyond its interpretive benefits, CLSM-FL ensures resilient performance in non-IID scenarios and against data-perturbed adversarial attacks. We substantiate its efficacy through extensive experiments covering various scenarios and data sets. This contribution not only underscores the crucial role of clustering in FL but also advances comprehension and practical application of FL methodologies in non-IID IoT environments.