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Large scale language models (LLM) have received significant attention andfound diverse applications across various domains, but their developmentencounters challenges in real-world scenarios. These challenges arise due tothe scarcity of public domain data availability and the need to maintainprivacy with respect to private domain data. To address these issues, federatedlearning (FL) has emerged as a promising technology that enables collaborativetraining of shared models while preserving decentralized data. We propose theconcept of federated LLM, which comprises three key components, i.e., federatedLLM pre-training, federated LLM fine-tuning, and federated LLM promptengineering. For each component, we discuss its advantage over traditional LLMtraining methods and propose specific engineering strategies forimplementation. Furthermore, we explore the novel challenges introduced by theintegration of FL and LLM. We analyze existing solutions and identify potentialobstacles faced by these solutions within the context of federated LLM.

Federated Large Language Model: A Position Paper