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.

提出了基于需求量化的移动边缘网络中的按需量化能效联合扩散方法，能够显著降低系统能耗和传输模型大小，同时有效地保持生成数据的合理质量和多样性。

移动边缘网络中绿色联邦生成扩散的按需量化

On-demand Quantization for Green Federated Generative Diffusion in  Mobile Edge Networks

Distributed Artificial Intelligence (AI) model training over mobile edge
networks encounters significant challenges due to the data and resource
heterogeneity of edge devices. The former hampers the convergence rate of the
global model, while the latter diminishes the devices' resource utilization
efficiency. In this paper, we propose a generative AI-empowered federated
learning to address these challenges by leveraging the idea of FIlling the
MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a
resource-aware data augmentation method that effectively mitigates the data
heterogeneity while ensuring efficient FL training. We first quantify the
relationship between the training data amount and the learning performance. We
then study the FIMI optimization problem with the objective of minimizing the
device-side overall energy consumption subject to required learning performance
constraints. The decomposition-based analysis and the cross-entropy searching
method are leveraged to derive the solution, where each device is assigned
suitable AI-synthesized data and resource utilization policy. Experiment
results demonstrate that FIMI can save up to 50% of the device-side energy to
achieve the target global test accuracy in comparison with the existing
methods. Meanwhile, FIMI can significantly enhance the converged global
accuracy under the non-independently-and-identically distribution (non-IID)
data.

分布式人工智能模型训练通过移动边缘网络面临着数据和资源异构性的挑战。本文提出了一种生成式 AI 增强的联邦学习方法来解决这些挑战，通过利用本地数据的填充缺失（FIMI）思想，有效减轻数据异构性并确保高效的 FL 训练。实验证明，FIMI 可以节省多达 50% 的设备能源，并能显著提高非独立同分布数据的全局准确性。