Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models like generative diffusion models in mobile edge networks. Additionally, the substantial energy consumption associated with training diffusion-based models, along with the limited resources of edge devices and complexities of network environments, pose challenges for improving the training efficiency of GAI models. To address this challenge, we propose an on-demand quantized energy-efficient federated diffusion approach for mobile edge networks. Specifically, we first design a dynamic quantized federated diffusion training scheme considering various demands from the edge devices. Then, we study an energy efficiency problem based on specific quantization requirements. Numerical results show that our proposed method significantly reduces system energy consumption and transmitted model size compared to both baseline federated diffusion and fixed quantized federated diffusion methods while effectively maintaining reasonable quality and diversity of generated data.

翻译：生成式人工智能（GAI）在移动边缘网络中展现出显著的生产力和创造力，例如元宇宙和工业物联网领域。联邦学习因其数据分布特性，成为在移动边缘网络中有效训练GAI模型的一种有前景的技术。然而，在移动边缘网络中训练大规模GAI模型（如生成式扩散模型）时，存在显著的通信消耗问题。此外，训练基于扩散的模型所伴随的高能耗、边缘设备的有限资源以及网络环境的复杂性，给提升GAI模型的训练效率带来了挑战。为解决这一挑战，我们提出了一种面向需求的量化节能联邦扩散方法，适用于移动边缘网络。具体而言，我们首先设计了一种考虑边缘设备不同需求的动态量化联邦扩散训练方案。然后，我们基于特定的量化需求研究了能效问题。数值结果表明，与基准联邦扩散方法和固定量化联邦扩散方法相比，我们提出的方法显著降低了系统能耗和传输模型大小，同时有效维持了生成数据的合理质量和多样性。