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Federated Learning for Feature Generalization with Convex Constraints

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arXiv:2606. 14416v1 Announce Type: cross Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.

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Authors: Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim
Released: 2026-06-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Federated Learning for Feature Generalization with Convex Constraints 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, under, line, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.14416

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