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Guangyuan Liu - IEEE Xplore Author Profile

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Author details

Guangyuan Liu

Publications

8

Citations

19

Publications by Year

20202025

Co-Authors:

Bo AiHongyang DuHanyue GuoJiawen KangDong In Kim

Show All Co-Authors (18)

Guangyuan Liu

Affiliation

College of Computing and Data Science

the Energy Research Institute @ NTU

Interdisciplinary Graduate Program, Nanyang Technological University, Singapore

Publication Topics

Deep Reinforcement Learning,
Data Security,
Diffusion Model,
Pose Estimation,
Resource Allocation,
Semantic Communication,
AI Models,
Bit Error Rate,
Central Server,
Channel Gain,
Deep Q-network,
Depth Map

Biography

Guangyuan Liu (liug0022@e.ntu.edu.sg) is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering, Energy Research Institute @ NTU, Interdisciplinary Graduate Program, Nanyang Technological University, Singapore. His research interests include generative AI, semantic communications, and Metaverse.(Based on document published on 11 January 2024).

Publications

8

Citations

19

Publications by Year

20202025

Co-Authors:

Bo Ai
Hongyang Du
Hanyue Guo
Jiawen Kang
Dong In Kim

Show All Co-Authors (18)

Author's Published Works

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Showing 1-8 of 8 results

Conferences (6)

Early Access Articles (1)

Magazines (1)

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Guangyuan Liu(8)
Dusit Niyato(5)
Hongyang Du(5)
Dong In Kim(4)
Zehui Xiong(4)
College of Computing and Data Science, Nanyang Technological University, Singapore(2)
Sungkyunkwan University, Korea(1)
School of Automation, Guangdong University of Technology, China(1)
Energy Research Institute@NTU, Interdisciplinary Graduate Program, School of Computer Science and Engineering, Nanyang Technological University, Singapore(1)
School of Computer Science and Engineering and the Energy Research Institute @ NTU, Interdisciplinary Graduate Program, Nanyang Technological University, Jurong West, Singapore(1)
2020 7th International Forum on Electrical Engineering and Automation (IFEEA)(1)
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)(1)
2024 International Wireless Communications and Mobile Computing (IWCMC)(1)
GLOBECOM 2023 - 2023 IEEE Global Communications Conference(1)
GLOBECOM 2024 - 2024 IEEE Global Communications Conference(1)
IEEE(8)
Ayia Napa, Cyprus(1)
Cape Town, South Africa(1)
Chongqing, China(1)
Hefei, China(1)
Kuala Lumpur, Malaysia(1)
Deep Reinforcement Learning(4)
Data Security(3)
Diffusion Model(3)
Pose Estimation(3)
Resource Allocation(3)

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

Guangyuan Liu;Hongyang Du;Jiacheng Wang;Dusit Niyato;Dong In Kim

IEEE Transactions on Mobile Computing

Year: 2025 | Early Access Article |

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant chall...Show More

Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

Guangyuan Liu;Hongyang Du;Jiacheng Wang;Dusit Niyato;Dong In Kim

IEEE Transactions on Mobile Computing

Year: 2025 | Early Access Article |

Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Minyang Li;Xiangyun Tang;Tao Zhang;Guangyuan Liu;Yu Weng

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

Year: 2024 | Conference Paper |

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Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have pr...Show More

Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Minyang Li;Xiangyun Tang;Tao Zhang;Guangyuan Liu;Yu Weng

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

Year: 2024 | Conference Paper |

Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Hongyang Du;Guangyuan Liu;Yijing Lin;Dusit Niyato;Jiawen Kang;Zehui Xiong;Dong In Kim

2024 International Wireless Communications and Mobile Computing (IWCMC)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can ...Show More

Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Hongyang Du;Guangyuan Liu;Yijing Lin;Dusit Niyato;Jiawen Kang;Zehui Xiong;Dong In Kim

2024 International Wireless Communications and Mobile Computing (IWCMC)

Year: 2024 | Conference Paper |

Generative Al-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts

Hongyang Du;Guangyuan Liu;Dusit Niyato;Jiayi Zhang;Jiawen Kang;Zehui Xiong;Bo Ai;Dong In Kim

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (6)

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Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders—and the subsequent deployment in network devices—are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities ...Show More

Generative Al-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts

Hongyang Du;Guangyuan Liu;Dusit Niyato;Jiayi Zhang;Jiawen Kang;Zehui Xiong;Bo Ai;Dong In Kim

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

Guangyuan Liu;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Dong In Kim;Xuemin Shen

Year: 2024 | Volume: 38, Issue: 5 | Magazine Article |

Cited by: Papers (10)

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Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Partic...Show More

Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

Guangyuan Liu;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Dong In Kim;Xuemin Shen

Year: 2024 | Volume: 38, Issue: 5 | Magazine Article |

Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Guangyuan Liu;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Boon Hee Soong

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering re...Show More

Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Guangyuan Liu;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Boon Hee Soong

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

Year: 2023 | Conference Paper |

Deposit Certificate and Sharing System of Threat Intelligence

Guangyuan Liu;Xiaoyu Ma;Jiting Zhou;Hanyue Guo

2020 7th International Forum on Electrical Engineering and Automation (IFEEA)

Year: 2020 | Conference Paper |

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In the digital age, malicious attacks have caused incalculable losses to individuals and enterprises. The traditional centralized preservation of threat intelligence is easy to be attacked, resulting in malicious tampering of intelligence, unequal threat intelligence, and low efficiency of intelligence transmission channel. Based on the blockchain technology, this paper registers the threat intell...Show More

Deposit Certificate and Sharing System of Threat Intelligence

Guangyuan Liu;Xiaoyu Ma;Jiting Zhou;Hanyue Guo

2020 7th International Forum on Electrical Engineering and Automation (IFEEA)

Year: 2020 | Conference Paper |

Classification and Sharing Method of Malware Based on Threat Intelligence

Guangyuan Liu;Jiting Zhou;Xiaoyu Ma

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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In recent years, the number of malware continues to grow, which has a huge impact on individuals, enterprises and so on. In this paper, a method of malware classification and sharing based on Threat Intelligence is proposed, which transforms malware into gray-scale image and uses broad learning system to classify malware. we design a malware intelligence storage system based on IPFS and Ethereum s...Show More

Classification and Sharing Method of Malware Based on Threat Intelligence

Guangyuan Liu;Jiting Zhou;Xiaoyu Ma

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

Year: 2020 | Volume: 1 | Conference Paper |

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