Currently, the generative model has garnered considerable attention due to
its application in addressing the challenge of scarcity of abnormal samples in
the industrial Internet of Things (IoT). However, challenges persist regarding
the edge deployment of generative models and the optimization of joint edge
AI-generated content (AIGC) tasks. In this paper, we focus on the edge
optimization of AIGC task execution and propose GMEL, a generative model-driven
industrial AIGC collaborative edge learning framework. This framework aims to
facilitate efficient few-shot learning by leveraging realistic sample synthesis
and edge-based optimization capabilities. First, a multi-task AIGC
computational offloading model is presented to ensure the efficient execution
of heterogeneous AIGC tasks on edge servers. Then, we propose an
attention-enhanced multi-agent reinforcement learning (AMARL) algorithm aimed
at refining offloading policies within the IoT system, thereby supporting
generative model-driven edge learning. Finally, our experimental results
demonstrate the effectiveness of the proposed algorithm in optimizing the total
system latency of the edge-based AIGC task completion.

提出了一种基于生成模型的工业 AIGC 协同边缘学习框架，通过利用真实样本合成和基于边缘的优化功能，实现高效的少样本学习，包括多任务 AIGC 计算卸载模型和注意力增强的多智能体强化学习算法，最终实现了边缘化 AIGC 任务完成的系统延迟优化。