Paper

Range Penalization: Theoretical Insights with Applications in Federated Learning

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arXiv:2606. 10916v1 Announce Type: new Abstract: 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.

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Authors: Yiyuan She, Zhaojun Hu, Yifan Sun
Released: 2026-06-10 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Range Penalization: Theoretical Insights with Applications in Federated Learning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, persona, source, line, rate. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.10916

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