OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

Abstract: Trained on massive publicly available data, 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.

• The paper introduces OpenFedLLM, which applies federated instruction tuning and value alignment to train LLMs on decentralized private data.
• It demonstrates that federated learning algorithms consistently outperform local training, with financial domain models even surpassing GPT-4 benchmarks.
• The study highlights future challenges such as managing heterogeneous data and advancing personalized federated learning to enhance model robustness and privacy.

Exploring Federated Learning for Training LLMs on Private Data: Insights from OpenFedLLM

Introduction to Federated Learning for LLMs

The pursuit of more sophisticated and efficient LLMs has led to the exploration of federated learning (FL) as a means to train these models on decentralized, private data. This approach not only circumvents the looming shortage of high-quality public datasets but also leverages the wealth of underutilized private data across various domains, adhering to privacy considerations and regulations. The innovative framework OpenFedLLM spearheads this exploration, integrating federated instruction tuning (FedIT) and federated value alignment (FedVA) into the training process of LLMs, along with providing support for a multitude of FL algorithms and datasets.

Federated Instruction Tuning and Value Alignment

OpenFedLLM distinguishes itself by enabling federated instruction tuning, whereby LLMs learn to follow human instructions accurately, and federated value alignment, infusing human values into the models. The framework's architecture is meticulously designed to facilitate seamless integration with standard FL protocols and various parameter-efficient fine-tuning techniques. Through extensive experiments across diverse domains, OpenFedLLM demonstrates that FL algorithms consistently outperform local training, showcasing the effectiveness of collaborative training in enhancing the performance of LLMs.

The research also reveals interesting findings in domain-specific benchmarks, for instance, models fine-tuned on financial datasets via FL managed to surpass GPT-4's performance, highlighting the strong motivation for adopting FL. Furthermore, OpenFedLLM's empirical study sheds light on the varying effectiveness of different FL algorithms across scenarios, inviting future developments in more tailored FL methods for LLM training.

Implications and Future Directions

The successful implementation and promising results of OpenFedLLM pave the way for further explorations within the realms of privacy-preserving collaborative training of LLMs. The identification of multiple emerging challenges and potential research directions emphasizes the need for innovative solutions in data management, heterogeneous preferences, personalized federated learning, and robustness and security in FedLLM.

Especially notable is the potential of personalized federated learning to cater to specific domain expertise or ethical values among participating clients, offering a pathway towards more nuanced and effective collaborative training processes. Moreover, the discussions around enhancing efficiency and extending the applicability of FedLLM to cross-silo and cross-device settings signal the broadening horizon of FL applications in training state-of-the-art LLMs.

Conclusion

This comprehensive exploration of utilizing federated learning for LLM training, epitomized by the OpenFedLLM framework, posits a substantial step forward in leveraging private distributed data for advancing the capabilities of LLMs. Beyond yielding improved model performances across various domains, the findings from OpenFedLLM underscore the importance of collaborative efforts in surmounting the impending scarcity of public datasets. The articulated challenges and future research avenues highlight the dynamic and evolving landscape of federated learning in LLM training, heralding a new era of privacy-conscious, collaborative AI development.