Interaction-Aware Gaussian Weighting for Clustered Federated Learning

Federated Learning (FL) emerged as a decentral- ized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance, which degrade model performance. Clustered FL bal- ances personalization and decentralized training by grouping clients with analogous data distribu- tions, enabling improved accuracy while adhering to privacy constraints. This approach effectively mitigates the adverse impact of heterogeneity in FL. In this work, we propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution, allowing training of a more ro- bust and personalized model on the identified clus- ters. FedGWC identifies homogeneous clusters by transforming individual empirical losses to model client interactions with a Gaussian reward mecha- nism. Additionally, we introduce the Wasserstein Adjusted Score, a new clustering metric for FL to evaluate cluster cohesion with respect to the individual class distribution. Our experiments on benchmark datasets show that FedGWC outper- forms existing FL algorithms in cluster quality and classification accuracy, validating the efficacy of our approach. Code is available at https: //github.com/davedleo/FedGWC