Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions

A new survey from arXiv (2608.13844v1) reviews federated prompt learning (FPL), a method that combines federated learning with large language models to enable privacy-preserving training without sharing raw data. The survey systematically examines FPL methods across pre-training, fine-tuning, and applications, analyzing trade-offs in performance, communication efficiency, and scalability, and identifies remaining security, privacy, and robustness challenges.

arXiv:2608.13844v1 Announce Type: new Abstract: Large language models LLMs have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns. Federated learning FL offers a decentralized training paradigm that enables clients to collaboratively train a learning model without sharing raw data, making it a promising solution for privacy-preserving LLM training and reasoning. This paper presents a comprehensive survey of federated prompt learning FPL to review recent advances in integrating the federated learning paradigm and large language models, answering the following research questions: RQ1: The fundamental motivations, characteristics, and enabling technologies of FPL, and how it differs from conventional FL and full-model federated fine-tuning; RQ2: The trade-offs FPL approaches exhibit in performance, communication efficiency, computational overhead, scalability, personalization, and heterogeneity handling; RQ3: The remaining security, privacy, robustness, and system challenges, along with key future research directions. To this end, we systematically examine existing FPL methods across the full model lifecycle: pre-training, fine-tuning, and practical applications, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms. Finally, we highlight open challenges and future directions, aiming to help readers understand how the insights drive research in FPL.