Range Penalization: Theoretical Insights with Applications in Federated Learning
This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to quantization, coding, and resource efficiency. Our approach identifies features with shared weights across different clients and adaptively clusters the weights of personalized features at extreme values, a process we refer to as polar clustering. Theoretical analysis of the associated estimators poses significant challenges due to the seminorm nature and non-decomposability of the regularizer. We develop new proof techniques for the nonasymptotic analysis of statistical accuracy and faithful pattern recovery. M...
| Authors | Yiyuan She, Zhaojun Hu, Yifan Sun |
|---|---|
| Primary category | stat.ML |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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