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Clustered Federated Learning via Embedding Distributions

arXiv 2506.07769 ↗First posted 9 Jun 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) is a widely used framework for machine learning in distributed data environments where clients hold data that cannot be easily centralised, such as for data protection reasons. FL, however, is known to be vulnerable to non-IID data. Clustered FL addresses this issue by finding more homogeneous clusters of clients. We propose a novel one-shot clustering method, EMD-CFL, using the Earth Mover's distance (EMD) between data distributions in embedding space. We theoretically motivate the use of EMDs using results from the domain adaptation literature and demonstrate empirically superior clustering performance in extensive comparisons against 16 baselines and on a range of challenging datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 9 Jun 2025.

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References · 34

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22 more
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