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Federated Hash Projected Latent Factor Learning

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arXiv:2606. 26192v1 Announce Type: new Abstract: Hash Learning (HL) is an efficient representation learning approach that maps real-valued data into compact binary representations.

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Authors: Jialan He
Released: 2026-06-26 (13d ago)
Topic: Related to current project
Relation to my work: Read next because Federated Hash Projected Latent Factor 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: code, persona, rate, project, without, factor, leakage, capability. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26192

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