Edge-Centric Federated Learning for LLMs in Smart Manufacturing: Architectures, Challenges, and Opportunities

The integration of Large Language Models (LLMs) into Industrial IoT (IIoT) systems enables 30-50% faster fault diagnosis and 25% reduction in unplanned downtime through predictive maintenance and quality control. However, deploying LLMs on resource-constrained edge devices (e.g., <4 GB PLCs) faces challenges in real-time processing (<10 ms latency) and compliance with industrial privacy standards (IEC 62443/GDPR). Federated Learning (FL) emerges as a critical enabler, allowing distributed training across sensors, robots and PLCs without raw data sharing. This paper presents the first comprehensive survey and taxonomy for FL+LLM in manufacturing, validated through case studies across automotive, electronics and pharmaceutical production. We systematically analyze: (1) compressed architectures (e.g., TinyBERT achieving 4ms inference), (2) EMI-resistant protocols for factory floors (tolerating 25% packet loss), and (3) privacy-accuracy tradeoffs (e.g., homomorphic encryption adding <15% latency overhead). Key unresolved challenges include sub-5ms inference on legacy PLCs and cross-factory generalization under non-IID data. The work provides concrete design guidelines for implementing FL+LLM systems that meet Industry 4.0 requirements for security, reliability, and real-time performance.