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English(EN) Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

新框架支持LLM在保护隐私的同时协作处理学生数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新颖的联邦推理框架，旨在增强AI驱动的教育系统中的隐私保护。该框架支持多个大型语言模型（LLM），包括Llama 3.3 70B Instruct、GPT-4o mini和Claude 3 Haiku，在不直接访问敏感学生数据或专有模型细节的情况下进行协作。通过采用epsilon-local差分隐私和基于残差的聚合方法，该系统在保持跨各种教育基准的高诊断准确性的同时，保护了个人预测。 AI

影响 这种方法可以实现AI在敏感教育环境中更广泛、更注重隐私的部署。

排序理由 该集群包含一篇详细介绍新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架支持LLM在保护隐私的同时协作处理学生数据

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17 / 100
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Tool
该集群包含一篇详细介绍新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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High
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yagna Manasa Boyapati, Chong Yu, Tianyu Jiang, Justin Zhan ·

    用于认知诊断的隐私保护异构多LLM联邦推理

    arXiv:2609.02947v1 Announce Type: cross Abstract: Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. To overcome this, we propose a federated inference framework in which several commercial LLM APIs …

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