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This review aims to examine how federated large language models can be effectively utilized in industrial applications, focusing on potential benefits and challenges.

February 11, 2026Sensors3 citationsOpen Access

A Review of Federated Large Language Models for Industry 4.0

JFJing FengNational University of Defense Technology YZYujing ZhangNational University of Defense Technology GMGao MtNational University of Defense Technology

Key Points

This review aims to examine how federated large language models can be effectively utilized in industrial applications, focusing on potential benefits and challenges.
Comprehensive review of recent research on federated large language models.
Comparison of enabling techniques and system designs relevant to Industry 4.0.
Analysis of deployment strategies in the industrial context.
Identified challenges such as data privacy and communication overheads in federated learning.
Highlighted the need for robust synchronization methods in large-scale federated systems.
Discussed practical implications for integrating LLMs into manufacturing environments.

Abstract

Industry 4.0 envisions a highly interconnected, autonomous manufacturing ecosystem enabled by the Industrial Internet of Things, Cyber-Physical Systems, and Artificial Intelligence. The emergence of large language models introduces new capabilities for semantic-aware decision-making, cross-domain knowledge integration, and intelligent automation. However, privacy, security, and regulatory constraints often isolate industrial data, impeding the scalability of LLMs in manufacturing. Federated learning addresses this by enabling decentralized LLM optimization without exposing raw data. This paper presents a comprehensive review of recent federated large language model research with a focus on industrial feasibility, comparing enabling techniques, system designs, and deployment strategies. Based on existing studies, forward-looking analyses are provided to highlight potential challenges and trade-offs in practical adoption, including computation and communication overheads, synchronization in large-scale federations, and system robustness. By bridging foundational methods with emerging industrial scenarios, we finally discuss the significant challenges associated with deploying federated large language models in complex industrial environments and outline a future research agenda.

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Cite This Study

Feng et al. (Mon,) studied this question.

synapsesocial.com/papers/698c1cb3267fb587c655f426 https://doi.org/https://doi.org/10.3390/s26041116

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