联邦提示学习：统一框架、实证分析与未来方向

中文摘要 AI 辅助

大语言模型（LLMs）已成为学术界和工业界基于云的智能服务的核心组件，但其训练与部署受限于高计算成本、数据集中化及隐私问题。联邦学习（FL）提供了一种去中心化训练范式，使客户端能在不共享原始数据的情况下协同训练学习模型，成为保护隐私的LLM训练与推理的有前景方案。本文对联邦提示学习（FPL）展开全面综述，梳理联邦学习范式与大语言模型融合的最新进展，解答以下研究问题：RQ1：FPL的基本动机、特征、使能技术，及其与传统FL、全模型联邦微调的差异；RQ2：FPL方法在性能、通信效率、计算开销、可扩展性、个性化及异构性处理方面的权衡；RQ3：现存的安全、隐私、鲁棒性及系统挑战，以及关键未来研究方向。为此，我们系统考察了现有FPL方法在全模型生命周期（预训练、微调、实际应用）中的情况，同时探讨安全、隐私与鲁棒性问题，总结现有防御机制。最后，我们强调开放挑战与未来方向，旨在帮助读者理解这些见解如何推动FPL领域的研究。

英文摘要

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.

发表机构

• Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学先进技术网络空间研究院）
• Huangpu Research School of Guangzhou University（广州大学黄埔研究院）
• Iwate Biotechnology Research Center（岩手生物技术研究中心）
• Nanjing University of Posts and Telecommunications（南京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。