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

no code implementations • 7 Mar 2024 • Bingkun Lai, Jiayi He, Jiawen Kang, Gaolei Li, Minrui Xu, Tao Zhang, Shengli Xie

Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution.

Federated Learning Quantization

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

no code implementations • 19 Dec 2023 • Bingkun Lai, Jinbo Wen, Jiawen Kang, Hongyang Du, Jiangtian Nie, Changyan Yi, Dong In Kim, Shengli Xie

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.

Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness

no code implementations • 29 Jul 2023 • Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai, Tianhao Wu, Zehui Xiong, Jiangtian Nie, Dusit Niyato, Yang Zhang, Shengli Xie

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services.

Decision Making Federated Learning +1