Abstract
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.
Abstract (translated)
目前，由于其在解决工业物联网（IoT）中异常样本 scarcity 的问题而受到了相当的关注。然而，关于生成模型的边缘部署和联合边缘 AI 生成的内容（AIGC）任务的优化问题仍然存在挑战。在本文中，我们重点关注了 AIGC 任务执行的边缘优化，并提出了 GMEL，一种基于生成模型的工业 AIGC 协同边缘学习框架。这个框架旨在通过利用真实的样本合成和边缘优化的能力来促进高效的少样本学习。首先，我们提出了一个多任务 AIGC 计算卸载模型，以确保在边缘服务器上高效执行异构性的 AIGC 任务。然后，我们提出了一种注意力增强的多代理强化学习（AMARL）算法，旨在在物联网系统内优化卸载策略，从而支持基于生成模型的边缘学习。最后，我们的实验结果证明了所提出的算法的有效性，即在优化基于边缘的 AIGC 任务完成时，可以降低系统的延迟总和。
URL
https://arxiv.org/abs/2405.02972
