Federated clustering effectively addresses the Non-IID problem by organizing clients into clusters for the training of personalized models. However, current federated clustering methods often cluster clients based on a single dimension, and fail to simultaneously achieve low computational cost, high accuracy, and strong privacy preservation. To address this problem, this manuscript proposes a novel approach called Gravitational Clustering Federated Learning (GCFL). GCFL treats each client as an object in a latent space, where the position encodes the local model and the mass encodes client importance. By simulating gravitational interactions between clients, GCFL enables adaptive clustering. Extensive experiments on Non-IID datasets validate the effectiveness of GCFL, and comparative analysis with state-of-the-art methods demonstrates that the proposed approach achieves more reasonable clustering and faster convergence.
Lu et al. (Sun,) studied this question.