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.

翻译：当前，生成式模型因其在解决工业物联网异常样本稀缺问题中的应用而受到广泛关注。然而，生成式模型的边缘部署以及联合边缘AI生成内容任务优化仍面临挑战。本文聚焦于AIGC任务执行的边缘优化问题，提出GMEL——一种生成式模型驱动的工业AIGC协作边缘学习框架。该框架旨在通过利用真实样本合成与边缘优化能力，促进高效的小样本学习。首先，提出多任务AIGC计算卸载模型，确保异构AIGC任务在边缘服务器上的高效执行。随后，提出注意力增强的多智能体强化学习算法，用于优化物联网系统中的卸载策略，从而支持生成式模型驱动的边缘学习。最后，实验结果表明，所提算法在优化基于边缘的AIGC任务完成总系统延迟方面具有有效性。