Scholarlink

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as the lifeblood of model performance, necessary data cannot always be centralized due to various factors such as privacy, regulation, geopolitics, copyright issues, and the sheer effort required to move vast datasets. In this paper, we explore how federated learning enabled by NVIDIA FLARE can address these challenges with easy and scalable integration capabilities, enabling parameter-efficient and full supervised fine-tuning of LLMs for natural language processing and biopharmaceutical applications to enhance their accuracy and robustness.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Empowering Federated Learning for Massive Models with NVIDIA FLARE

  • 章节

  • 发表日期：

    2025年

查看全文
  • Holger R. Roth,
  • Ziyue Xu,
  • Yuan-Ting Hsieh,
  • Adithya Renduchintala,
  • Isaac Te-Chung Yang,
  • Zhihong Zhang,
  • Yuhong Wen,
  • Sean Yang,
  • Kevin Lu,
  • Kristopher Kersten,
  • Camir Ricketts,
  • Daguang Xu,
  • Chester Chen,
  • Yan Cheng,
  • Andrew Feng

摘要

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as the lifeblood of model performance, necessary data cannot always be centralized due to various factors such as privacy, regulation, geopolitics, copyright issues, and the sheer effort required to move vast datasets. In this paper, we explore how federated learning enabled by NVIDIA FLARE can address these challenges with easy and scalable integration capabilities, enabling parameter-efficient and full supervised fine-tuning of LLMs for natural language processing and biopharmaceutical applications to enhance their accuracy and robustness.