Large scale language models (LLM) have received significant attention and found diverse applications across various domains, but their development encounters challenges in real-world scenarios. These challenges arise due to the scarcity of public domain data availability and the need to maintain privacy with respect to private domain data. To address these issues, federated learning (FL) has emerged as a promising technology that enables collaborative training of shared models while preserving decentralized data. We propose the concept of federated LLM, which comprises three key components, i.e., federated LLM pre-training, federated LLM fine-tuning, and federated LLM prompt engineering. For each component, we discuss its advantage over traditional LLM training methods and propose specific engineering strategies for implementation. Furthermore, we explore the novel challenges introduced by the integration of FL and LLM. We analyze existing solutions and identify potential obstacles faced by these solutions within the context of federated LLM.

提出了联邦式大规模语言模型（LLM）的概念，并讨论了其三个关键组成部分：联邦式LLM预训练、联邦式LLM微调和联邦式LLM提示工程。还探讨了联邦学习和大规模语言模型整合所带来的新挑战，并分析了现有解决方案和潜在障碍。

联邦大型语言模型：一篇立场论文