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

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

Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study

As the next-generation wireless communication system, Sixth-Generation (6G) technologies are emerging, enabling various mobile edge networks that can revolutionize wireless communication and connectivity. By integrating Generative Artificial Intelligence (GAI) with mobile edge networks, generative mobile edge networks possess immense potential to enhance the intelligence and efficiency of wireless communication networks. In this article, we propose the concept of generative mobile edge networks and overview widely adopted GAI technologies and their applications in mobile edge networks. We then discuss the potential challenges faced by generative mobile edge networks in resource-constrained scenarios. To address these challenges, we develop a universal resource-efficient generative incentive mechanism framework, in which we design resource-efficient methods for network overhead reduction, formulate appropriate incentive mechanisms for the resource allocation problem, and utilize Generative Diffusion Models (GDMs) to find the optimal incentive mechanism solutions. Furthermore, we conduct a case study on resource-constrained mobile edge networks, employing model partition for efficient AI task offloading and proposing a GDM-based Stackelberg model to motivate edge devices to contribute computing resources for mobile edge intelligence. Finally, we propose several open directions that could contribute to the future popularity of generative mobile edge networks

Coupled Surface-Confinement Effect and Pore Engineering in a Single-Fe-Atom Catalyst for Ultrafast Fenton-like Reaction with High-Valent Iron-Oxo Complex Oxidation

The nanoconfinement effect in Fenton-like reactions shows great potential in environmental remediation, but the construction of confinement structure and the corresponding mechanism are rarely elucidated systematically. Herein, we proposed a novel peroxymonosulfate (PMS) activation system employing the single Fe atom supported on mesoporous N-doped carbon (FeSA-MNC, specific surface area = 1520.9 m2/g), which could accelerate the catalytic oxidation process via the surface-confinement effect. The degradation activity of the confined system was remarkably increased by 34.6 times compared to its analogue unconfined system. The generation of almost 100% high-valent iron-oxo species was identified via 18O isotope-labeled experiments, quenching tests, and probe methods. The density functional theory illustrated that the surface-confinement effect narrows the gap between the d-band center and Fermi level of the single Fe atom, which strengthens the charge transfer rate at the reaction interface and reduces the free energy barrier for PMS activation. The surface-confinement system exhibited excellent pollutant degradation efficiency, robust resistance to coexisting matter, and adaptation of a wide pH range (3.0–11.0) and various temperature environments (5–40 °C). Finally, the FeSA-MNC/PMS system could achieve 100% sulfamethoxazole removal without significant performance decline after 10,000-bed volumes. This work provides novel and significant insights into the surface-confinement effect in Fenton-like chemistry and guides the design of superior oxidation systems for environmental remediation