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Life sciences · Preprint
arXiv · August 14, 2026
Raises a question worth testing. It does not answer one.
This is a narrative survey of federated prompt learning that synthesizes existing literature on combining federated learning with large language models. It poses research questions about FPL characteristics, performance trade-offs, and security challenges, but presents no original empirical findings or comparative results from the authors.
Preprint.
Federated learning is positioned as a decentralized training paradigm that enables collaborative model training without sharing raw data FPL integrates federated learning paradigm with large language models across pre-training, fine-tuning, and practical applications Survey identifies security, privacy, robustness, and system challenges as remaining open problems
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
A survey paper that frames research questions and reviews existing methods without presenting new empirical results, original data, or comparative evidence from the authors' own work.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
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.
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