Empowering Federated Learning for Massive Models with NVIDIA FLARE
Holger R. Roth Ziyue Xu Yuan-Ting Hsieh Adithya Renduchintala Isaac Yang Zhihong Zhang Yuhong Wen Sean Yang Kevin Lu Kristopher Kersten Camir Ricketts Daguang Xu Chester Chen Yan Cheng Andrew Feng
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.