使用NVIDIA FLARE赋能大规模模型的联邦学习
Empowering Federated Learning for Massive Models with NVIDIA FLARE
AI总结：
本文探讨了如何利用NVIDIA FLARE实现的联邦学习解决数据无法集中化的问题，通过简单可扩展的集成能力对大语言模型进行参数高效和全量监督微调，以提升自然语言处理和生物制药应用的准确性与鲁棒性。
摘要：
在人工智能（AI）和大语言模型（LLM）不断演进的背景下，有效处理和利用数据已成为一项关键挑战。大多数最先进的机器学习算法是以数据为中心的。然而，作为模型性能命脉的必要数据，由于隐私、监管、地缘政治、版权问题以及移动庞大数据集所需的巨大工作量等各种因素，并不总是能够被集中起来。在本文中，我们探讨了由NVIDIA FLARE实现的联邦学习如何通过简单且可扩展的集成能力来应对这些挑战，从而实现对LLM的参数高效和全量监督微调，以用于自然语言处理和生物制药应用，进而提升其准确性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
