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Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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A Review of Federated Large Language Models for Industry 4.0.

Feng Jing¹, Yujing Zhang¹, Mei Gao¹

¹Test Center, National University of Defense Technology, Xi'an 710106, China.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

Federated learning enables large language models (LLMs) for Industry 4.0 by optimizing them on decentralized data, enhancing manufacturing intelligence while preserving privacy. This review explores techniques, challenges, and future directions for industrial LLM adoption.

Keywords:

federated learning industry 4.0 large language model

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Area of Science:

Manufacturing Technology
Artificial Intelligence
Data Science

Background:

Industry 4.0 relies on interconnected systems like Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS).
Large Language Models (LLMs) offer advanced capabilities for decision-making and automation in manufacturing.
Industrial data privacy, security, and regulations hinder LLM scalability.

Purpose of the Study:

To review federated large language model research for industrial applications.
To compare techniques, system designs, and deployment strategies for federated LLMs in manufacturing.
To identify challenges and future research directions for practical adoption.

Main Methods:

Comprehensive literature review of federated large language model research.
Analysis of enabling techniques, system architectures, and deployment strategies.
Forward-looking analysis of practical adoption challenges and trade-offs.

Main Results:

Federated learning allows decentralized LLM optimization without raw data sharing.
Identified key challenges include computational/communication overheads and synchronization in large-scale systems.
Compared various approaches for industrial feasibility.

Conclusions:

Federated LLMs hold significant potential for Industry 4.0 but face practical deployment challenges.
Addressing overheads, synchronization, and robustness is crucial for complex industrial environments.
Further research is needed to bridge foundational methods with industrial scenarios.