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<p>Clustering clients into groups with relatively homogeneous data distributions is a key strategy for improving federated learning under non-independent and identically distributed data. However, most state-of-the-art clustering approaches require clients to possess labeled datasets and perform substantial local computation, limiting their applicability in real-world settings. To address these limitations, we introduce <span>CoLEDS</span>, a method for profiling unlabeled client datasets with minimal computational overhead. <span>CoLEDS</span> trains a model using a contrastive learning objective defined across multiple clients and optimized in a distributed fashion through joint client–server coordination. The resulting model embeds key properties of client datasets into low-dimensional vectors that are shared with the server for clustering. Extensive empirical evaluation shows that these profiles accurately capture latent dataset characteristics. By clustering clients based on these representations, <span>CoLEDS</span> yields federatively trained models that are better aligned with individual data distributions and enables appropriate model assignment even for clients that do not participate in federated training.</p>

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Label-free dataset profiling for federated client clustering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Boris Radovič,
  • Marco Canini,
  • Veljko Pejović

摘要

Clustering clients into groups with relatively homogeneous data distributions is a key strategy for improving federated learning under non-independent and identically distributed data. However, most state-of-the-art clustering approaches require clients to possess labeled datasets and perform substantial local computation, limiting their applicability in real-world settings. To address these limitations, we introduce CoLEDS, a method for profiling unlabeled client datasets with minimal computational overhead. CoLEDS trains a model using a contrastive learning objective defined across multiple clients and optimized in a distributed fashion through joint client–server coordination. The resulting model embeds key properties of client datasets into low-dimensional vectors that are shared with the server for clustering. Extensive empirical evaluation shows that these profiles accurately capture latent dataset characteristics. By clustering clients based on these representations, CoLEDS yields federatively trained models that are better aligned with individual data distributions and enables appropriate model assignment even for clients that do not participate in federated training.