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Updated: Jun 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

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通过无生成器的数据生成进行无数据的知识蒸,用于非IID联合学习的非IID联合学习

Siran Zhao1, Tianchi Liao2, Lele Fu3

  • 1Sun Yat-sen University, School of computer science and engineering, Guangzhou, China.

Neural networks : the official journal of the International Neural Network Society
|August 19, 2024
PubMed
概括

联合学习 (FL) 面临非IID数据挑战. 我们的FedF^2DG方法使用本地模型进行数据生成和知识蒸,改善了没有代理数据或生成器的全球模型性能.

关键词:
数据生成 数据生成数据异质性 数据异质性无数据的知识蒸.非IID的联合学习学习.

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