Empowering Federated Learning for Massive Models with NVIDIA FLARE

TL;DR

This paper demonstrates how NVIDIA FLARE facilitates scalable federated learning for large language models, enabling privacy-preserving, efficient fine-tuning in NLP and biopharmaceutical fields, overcoming data sharing barriers.

Contribution

It introduces a scalable federated learning framework with NVIDIA FLARE for fine-tuning massive models across diverse applications, emphasizing ease of integration and privacy preservation.

Findings

Effective fine-tuning of large models in privacy-sensitive environments

Enhanced model accuracy and robustness through federated learning

Scalable integration for NLP and biopharmaceutical applications

Abstract

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.