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Range Penalization: Theoretical Insights with Applications in Federated Learning

paperactiverange-penalization-theoretical-insights-with-applications-in-federated-learning-1e10800f·1 events·first seen Jun 10, 2026

Aliases: Range Penalization: Theoretical Insights with Applications in Federated Learning

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3arXiv · cs.LG·Jun 10, 2026·source ↗

Range Penalization for Federated Learning: Polar Clustering and Statistical Accuracy

This paper introduces range regularization for federated learning, identifying shared-weight features across clients while adaptively clustering personalized feature weights at extreme values (termed polar clustering). The approach targets statistical accuracy, cross-client regularity, and resource efficiency for quantization and coding. New nonasymptotic proof techniques are developed for the seminorm-based regularizer, alongside a fast optimization algorithm exploiting local strong convexity.

Inference Economics Range Penalization: Theoretical Insights with Applications in Federated Learning