DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910004004519936 |
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| author | Jaramillo-Civill, Mariona Wu, Peng Closas, Pau |
| author_facet | Jaramillo-Civill, Mariona Wu, Peng Closas, Pau |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_07132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering Jaramillo-Civill, Mariona Wu, Peng Closas, Pau Machine Learning Distributed, Parallel, and Cluster Computing 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. |
| title | DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.07132 |