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Research questionHow can federated PDE learning define and measure client heterogeneity across different equations?Federated PDE clients may differ in their physical responses and problem configurations, making non-IID data difficult to define consistently across equations. Without a transferable measure, cross-task comparisons and explanations of divergent local updates remain difficult.
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Latest papersRecent research connected to this question, newest first.Solution-space heterogeneity shapes federated learning dynamics across partial differential equationsApplies to federated scientific machine learning with neural surrogates for partial differential equations. Evidence covers seven controlled and public PDE tasks, three neural-operator families, and five random seeds; it shows consistent effects on solution distance and optimization heterogeneity, while final-error impacts remain task dependent.research paper · Sep 4, 2026
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