@article{Lu2026, 
author = {Guangxi Lu and Lizong Zhang and Chong Mu and Haoji Zhang},
title = {Adaptive Federated Clustering via Gravitational Dynamics},
year = {2026},
journal = {Tsinghua Science and Technology},
keywords = {Federated learning, Non-IID Problem, Adaptive Client Clustering, Personalized Models, Gravitational Dynamics},
url = {https://www.sciopen.com/article/10.26599/TST.2026.90100032},
doi = {10.26599/TST.2026.90100032},
abstract = {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.}
}