This post explores how federated learning (FL) enabled by NVIDIA FLARE can address data challenges in large language models. It discusses the benefits of supervised fine-tuning and parameter-efficient fine-tuning for adapting foundation models. The post also highlights the use of the Lightning Client API and the streaming capacity of NVIDIA FLARE for scalable model training. Performance results of Federated PEFT and SFT are presented, showcasing the advantages of federated learning for language model adaptation.
Table of contents
The data challengeFederated learningFoundation modelsFL for LLM adaptationsEasy adaptation using Lightning Client APIScalable model training through streamingFederated PEFT and SFT performanceConclusion20 Impressions