{"ID":2858840,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07132","arxiv_id":"2510.07132","title":"DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering","abstract":"Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a global model and fully personalized models. However, most CFL methods require the number of clusters K to be fixed a priori, which is impractical when the latent structure is unknown. We propose DPMM-CFL, a CFL algorithm that places a Dirichlet Process (DP) prior over the distribution of cluster parameters. This enables nonparametric Bayesian inference to jointly infer both the number of clusters and client assignments, while optimizing per-cluster federated objectives. This results in a method where, at each round, federated updates and cluster inferences are coupled, as presented in this paper. The algorithm is validated on benchmark datasets under Dirichlet and class-split non-IID partitions.","short_abstract":"Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a global model and fully personalized models. However, most CFL methods require the number of clusters K to be fixed a priori, which is impractic...","url_abs":"https://arxiv.org/abs/2510.07132","url_pdf":"https://arxiv.org/pdf/2510.07132v2","authors":"[\"Mariona Jaramillo-Civill\",\"Peng Wu\",\"Pau Closas\"]","published":"2025-10-08T15:27:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\",\"stat.ML\"]","methods":"[]","has_code":false}