Federated Prompt Learning Advances Privacy-Preserving LLM Training

Large Language Models (LLMs) are central to many cloud-based intelligent services, but their development and deployment face significant hurdles, including high computational costs, the need for centralized data, and critical privacy concerns. Federated Learning (FL) offers a compelling solution by allowing multiple clients to collaboratively train a model without directly exchanging their sensitive raw data, thereby enhancing privacy. This comprehensive survey delves into Federated Prompt Learning (FPL), a novel integration of FL with LLMs. The research explores the core reasons for FPL's emergence, its unique characteristics, and the underlying technologies that enable it, distinguishing it from conventional FL and full-model federated fine-tuning. It also meticulously examines the trade-offs inherent in FPL approaches, considering factors such as performance, communication efficiency, computational overhead, scalability, personalization, and handling data heterogeneity. Furthermore, the paper addresses the ongoing challenges related to security, privacy, and robustness within FPL, summarizing existing defense mechanisms. It concludes by highlighting open research questions and future directions, aiming to guide further advancements in this critical area for privacy-preserving LLM development and application.