Towards Federated Large Language Models: Motivations, Methods, and Future Directions

Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the paradigm of natural language comprehension and generation. Despite their impressive performance, these models still face certain challenges, including the need for extensive data, high computational resources, and privacy concerns related to their data sources. Recently, Federated Learning (FL) has surfaced as a cooperative AI methodology that enables AI training across distributed computation entities while maintaining decentralized data. Integrating FL with LLMs presents an encouraging solution for privacy-preserving and collaborative LLM learning across multiple end-users, thus addressing the aforementioned challenges. In this paper, we provide an exhaustive review of federated Large Language Models, starting from an overview of the latest progress in FL and LLMs, and proceeding to a discourse on their motivation and challenges for integration. We then conduct a thorough review of the existing federated LLM research from the perspective of the entire lifespan, from pre-training to fine-tuning and practical applications. Moreover, we address the threats and issues arising from this integration, shedding light on the delicate balance between privacy and robustness, and introduce existing approaches and potential strategies for enhancing federated LLM privacy and resilience. Finally, we conclude this survey by outlining promising avenues for future research in this emerging field.