This research introduces a novel LLM-empowered IoT architecture specifically designed for upcoming 6G networks, enabling unprecedented intelligent autonomy in IoT ecosystems.

• Proposes a comprehensive architecture integrating LLMs with IoT for intelligent decision-making and self-optimization
• Implements edge inference techniques and a split federated learning framework to overcome computational limitations
• Addresses key challenges including latency constraints, privacy concerns, and resource optimization for IoT environments
• Presents practical solutions for sustainable deployment of AI capabilities across distributed IoT networks

This breakthrough architecture matters for engineering by providing a viable framework for embedding advanced AI capabilities into resource-constrained IoT devices, potentially transforming how IoT systems operate in the 6G era.

LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions