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Updated: Jun 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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一种多项式代理模型方法,用于可验证的去中心化联合学习

Tan Li1, Samuel Cheng2, Tak Lam Chan3

  • 1Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China. Tan.li@cairs.hk.

Scientific reports
|November 20, 2024
PubMed
概括

ProxyZKP通过使用零知识证明和代理模型确保本地培训完整性来增强分散的联合学习. 这种新的框架提供了更快的证明生成,并保持了模型准确性与差异性隐私.

关键词:
计算完整性的计算完整性分散的联邦学习学习.零知识证明证明

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