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.

翻译：在人工智能和大语言模型不断发展的背景下，有效处理与利用数据已成为关键挑战。当前最先进的机器学习算法大多以数据为中心。然而，作为模型性能的命脉，必要的数据因隐私、法规、地缘政治、版权问题以及迁移海量数据集所需的大量工作等因素，往往无法集中化。本文探讨了通过 NVIDIA FLARE 实现的联邦学习如何凭借简便且可扩展的集成能力应对这些挑战，支持对自然语言处理和生物制药领域的大语言模型进行参数高效及全监督微调，以提升其准确性与鲁棒性。