Empowering Federated Learning for Massive Models with NVIDIA FLARE
This addresses data decentralization problems for AI practitioners working with large models, though it appears incremental as it applies an existing framework to new domains.
The paper tackles the challenge of training large language models when data cannot be centralized due to privacy and logistical constraints by using NVIDIA FLARE for federated learning, enabling parameter-efficient and full supervised fine-tuning to enhance accuracy and robustness in NLP and biopharmaceutical applications.
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.