AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Lai, B.; He, J.; Kang, J.; Li, G.; Xu, M.; Zhang, T.; Xie, S.

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

In: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, pp. 2883–2888, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 15503607 (ISSN); 978-172819054-9 (ISBN).

Abstract | Links | BibTeX | Tags: EDGE Networks, Energy Efficient, Federated Diffusion, Federated learning, Generative adversarial networks, Generative AI, Generative Diffusion, Intelligence models, Metaverses, On demands, On-demand Quantization, Quantisation

@inproceedings{lai_-demand_2024,

title = {On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks},

author = {B. Lai and J. He and J. Kang and G. Li and M. Xu and T. Zhang and S. Xie},

editor = {Valenti M. and Reed D. and Torres M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202836582&doi=10.1109%2fICC51166.2024.10622695&partnerID=40&md5=8318ef451bb946470eb34ef50d09b5a1},

doi = {10.1109/ICC51166.2024.10622695},

isbn = {15503607 (ISSN); 978-172819054-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Int Conf Commun},

pages = {2883–2888},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {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. © 2024 IEEE.},

keywords = {EDGE Networks, Energy Efficient, Federated Diffusion, Federated learning, Generative adversarial networks, Generative AI, Generative Diffusion, Intelligence models, Metaverses, On demands, On-demand Quantization, Quantisation},

pubstate = {published},

tppubtype = {inproceedings}

}