Exploring the intersection of generative models and edge AI, this paper outlines the development of GMEL, a collaborative edge-learning framework for enhancing task execution in industrial IoT. Details include:

• AIGC Computational Offloading: Introduces a multi-task computational model ensuring efficient execution on edge servers.
• AMARL Algorithm: Proposes an attention-enhanced multi-agent reinforcement learning algorithm, refining offloading policies.
• Real-World Impact: Demonstrated effectiveness in reducing total system latency and boosting task execution efficiency in edge environments.

Key Points:

• Singular focus on optimizing edge AI performance in heavily networked systems.
• Enhances the real-time execution capabilities of edge systems.

Significance: This research contributes to our understanding of the benefits of integrating advanced AI techniques in edge networks. It exemplifies the evolving role of AI agents in enhancing the efficiency of IoT systems and potentially setting a new standard for industry practices.