AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
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}
}
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.
Na, M.; Lee, J.
Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing Journal Article
In: Applied Sciences (Switzerland), vol. 14, no. 18, 2024, ISSN: 20763417 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence technologies, Augmented Reality, benchmarking, Computation offloading, Edge computing, Energy Efficient, Generative adversarial networks, Generative AI, Image enhancement, Mobile augmented reality, Mobile edge computing, Multi-access edge computing, Multiaccess, Quality of Service, Resolution process, super-resolution, Superresolution, Trade off
@article{na_generative_2024,
title = {Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing},
author = {M. Na and J. Lee},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205236316&doi=10.3390%2fapp14188419&partnerID=40&md5=0aa1c42cb7343cfb55a9dc1e66494dc6},
doi = {10.3390/app14188419},
issn = {20763417 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {14},
number = {18},
abstract = {This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. © 2024 by the authors.},
keywords = {Artificial intelligence technologies, Augmented Reality, benchmarking, Computation offloading, Edge computing, Energy Efficient, Generative adversarial networks, Generative AI, Image enhancement, Mobile augmented reality, Mobile edge computing, Multi-access edge computing, Multiaccess, Quality of Service, Resolution process, super-resolution, Superresolution, Trade off},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. © 2024 by the authors.