  Trained on massive publicly available data, large language models (LLMs) have
demonstrated tremendous success across various fields. While more data
contributes to better performance, a disconcerting reality is that high-quality
public data will be exhausted in a few years. In this paper, we offer a
potential next step for contemporary LLMs: collaborative and privacy-preserving
LLM training on the underutilized distributed private data via federated
learning (FL), where multiple data owners collaboratively train a shared model
without transmitting raw data. To achieve this, we build a concise, integrated,
and research-friendly framework/codebase, named OpenFedLLM. It covers federated
instruction tuning for enhancing instruction-following capability, federated
value alignment for aligning with human values, and 7 representative FL
algorithms. Besides, OpenFedLLM supports training on diverse domains, where we
cover 8 training datasets; and provides comprehensive evaluations, where we
cover 30+ evaluation metrics. Through extensive experiments, we observe that
all FL algorithms outperform local training on training LLMs, demonstrating a
clear performance improvement across a variety of settings. Notably, in a
financial benchmark, Llama2-7B fine-tuned by applying any FL algorithm can
outperform GPT-4 by a significant margin while the model obtained through
individual training cannot, demonstrating strong motivation for clients to
participate in FL. The code is available at
https://github.com/rui-ye/OpenFedLLM.
