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Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries

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arXiv:2606. 02958v1 Announce Type: new Abstract: Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters, activations, optimizer state, and per-device updates-cannot be exported outside an administrative boundary.

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Authors: Hina Dixit, Punit Kumar, Irene Tenison, Nevasini Sasikumar
Released: 2026-06-03 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries 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: text, under, eval, token, line, rate, control, sees. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.02958

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