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New MIXGUARD framework enhances privacy in LLM split learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced MIXGUARD, a new framework designed to enhance privacy in split learning for large language models (LLMs). This method employs token-level and representation-level obfuscation, along with adaptive gradient perturbation, to maintain learning signals while preventing data leakage to servers. Experiments demonstrate that MIXGUARD achieves utility comparable to non-split training and offers superior privacy protection against reconstruction attacks. AI

IMPACT Enhances privacy for LLM training by enabling split learning without significant utility degradation.

RANK_REASON The cluster contains an academic paper detailing a new method for LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Chen Chen, Xiang Gao, Xianshun Wang, Chengran Li, Shengyu Xia, Xueluan Gong, Linru Zhang, Qian Wang, Kwok-Yan Lam ·

    The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models

    arXiv:2606.16801v1 Announce Type: new Abstract: Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keeping raw data local. However, existing privacy-preserv…

  2. arXiv cs.CL TIER_1 English(EN) · Kwok-Yan Lam ·

    The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models

    Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keeping raw data local. However, existing privacy-preserving split learning methods still face a difficul…

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