DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering

• URL: http://arxiv.org/abs/2510.07132v1
• Date: Wed, 08 Oct 2025 15:27:08 GMT
• Title: DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
• Authors: Mariona Jaramillo-Civill, Peng Wu, Pau Closas,
• Abstract summary: Clustered Federated Learning improves performance under non-IID client heterogeneity.<n>Most CFL methods require the number of clusters K to be fixed a priori.<n>We propose DPMM-CFL, a CFL algorithm that places a Dirichlet Process (DP) prior over the distribution of cluster parameters.
• Score: 8.645893961456801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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