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

2026-08-13

AI Summary

This study addresses the computational bottlenecks and privacy risks inherent in large model training and deployment by presenting a systematic review of Federated Prompt Learning (FPL). Through a unified analytical framework, this work comprehensively examines performance-efficiency trade-offs and security defense mechanisms across the entire lifecycle, from pre-training to application. The research elucidates the current landscape and technical limitations of the field while identifying critical future directions. Ultimately, it provides both theoretical foundations and practical guidelines for achieving efficient large model adaptation alongside robust privacy protection, thereby advancing the development of safe and trustworthy artificial intelligence.

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.

Problem

• Federated Prompt Learning
• Large Language Models
• Privacy-preserving
• Communication Efficiency
• Open Challenges

Innovation

• Federated Prompt Learning
• Large Language Models
• Unified Framework
• Privacy-Preserving
• Trade-off Analysis