Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.079321
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

REVIEW

When Federated Learning Meets Large Language Models: Taxonomy, Challenges, and Opportunities

Shan Jiang1, Wenxin You2, Haoran Zhang3, Shichang Xuan3,*, Jiaxing Shen4
1 School of Software Engineering, Sun Yat-Sen University, Zhuhai, China
2 School of Art and Design, Guangzhou Institute of Science and Technology, Guangzhou, China
3 College of Computer Science and Technology, Harbin Engineering University, Harbin, China
4 School of Data Science, Lingnan University, Hong Kong SAR, China
* Corresponding Author: Shichang Xuan. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.079321

Received 19 January 2026; Accepted 20 April 2026; Published online 06 May 2026

Abstract

Large Language Models (LLMs) have been playing a transformative role in natural language understanding and generation, yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training. Federated Learning (FL) provides a promising alternative by enabling training LLMs collaboratively without sharing raw data. However, integrating FL and LLMs introduces new challenges, including model size, device heterogeneity, non-IID data, and alignment requirements. This survey offers a structured overview of the federated LLM ecosystem. We present a comprehensive taxonomy encompassing system architectures, advanced data strategies for addressing heterogeneity, and retrieval-augmented generation in federated contexts. Additionally, we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns. We conclude by summarizing emerging applications in healthcare, industry, software engineering, and finance, and by outlining open problems and research opportunities for scalable, secure, and responsible federated LLM deployment.

Keywords

Large language models; federated learning; foundation models; federated large language models

  • 172

    View

  • 25

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link