What question did this study set out to answer?

This survey aims to explore the integration of federated learning with large language models, focusing on their applications and related challenges.

May 8, 2026Computers, materials & continua/Computers, materials & continua (Print)Open Access

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

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This survey aims to explore the integration of federated learning with large language models, focusing on their applications and related challenges.
Review of federated learning and large language models (LLMs) integration.
Development of a comprehensive taxonomy considering system architectures and data strategies.
Analysis of adaptation methods and privacy concerns in resource-limited environments.
Outlined a taxonomy addressing challenges like model size and data heterogeneity.
Identified efficient adaptation strategies for LLMs on resource-constrained clients.
Highlighted emerging applications in sectors including healthcare and finance.

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Implication

Survey investigates federated learning's impact on adapting large language models for privacy-sensitive applications, indicating significant opportunities and challenges.

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/69fd7f25bfa21ec5bbf077c5 https://doi.org/10.32604/cmc.2026.079321

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Implication

Survey investigates federated learning's impact on adapting large language models for privacy-sensitive applications, indicating significant opportunities and challenges.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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