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.