On-demand Quantization for Green Federated Generative Diffusion in
  Mobile Edge Networks
Bingkun Lai Jiayi He Jiawen Kang Gaolei Li Minrui Xu Tao zhang Shengli Xie
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.