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Peichun Li - IEEE Xplore Author Profile
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Peichun Li

Publications
18
Citations
328
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (28)
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Peichun Li

Affiliation

Department of Computer and Information Science
State Key Laboratory of Internet of Things for Smart City
University of Macau, Macau, China

Publication Topics

Biography

Peichun Li (peichun.li@um.edu.mo) Is Currently A Research Assistant With The State Key Laboratory Of Internet Of Things For Smart City, University Of Macau, Macau, China. His Research Interests Include Edge Computing And Deep Learning, Particularly In Efficient Algorithms For Artificial Intelligence (Ai) Applications.(Based on document published on 9 October 2023).
Publications
18
Citations
328
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (28)
Author's Published Works

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Efficient Federated Learning With Quality-Aware Generated Models: An Incentive Mechanism

Hanwen Zhang;Peichun Li;Minghui Dai;Yuan Wu;Liping Qian

IEEE Internet of Things Journal
Year: 2025 | Volume: 12, Issue: 2 | Journal Article |
Federated learning (FL) encounters slow convergence due to data heterogeneity issues. Recently, generative artificial intelligence (AI) has showcased remarkable capabilities in synthesizing realistic data. To effectively address the challenges of nonindependent and identically distributed (non-IID) data, this article introduces a collaborative AI training framework that leverages generative AI to ...Show More

Efficient Federated Learning With Quality-Aware Generated Models: An Incentive Mechanism

Hanwen Zhang;Peichun Li;Minghui Dai;Yuan Wu;Liping Qian

Year: 2025 | Volume: 12, Issue: 2 | Journal Article |

FlexGen: Efficient On-Demand Generative AI Service With Flexible Diffusion Model in Mobile Edge Networks

Peichun Li;Huanyu Dong;Liping Qian;Sheng Zhou;Yuan Wu

IEEE Transactions on Cognitive Communications and Networking
Year: 2024 | Early Access Article |
Generative artificial intelligence (AI) in edge networks has excelled in delivering human-level creative services close to the end users. However, providing customized intelligence services to a wide range of end clients remains challenging due to the diverse demands of edge applications. In this paper, we present FlexGen, an efficient generative AI framework with flexible diffusion models, to tai...Show More

FlexGen: Efficient On-Demand Generative AI Service With Flexible Diffusion Model in Mobile Edge Networks

Peichun Li;Huanyu Dong;Liping Qian;Sheng Zhou;Yuan Wu

Year: 2024 | Early Access Article |

Snowball Effect in Federated Learning: An Approach of Exponentially Expanding Structures for Optimizing the Training Efficiency

Guoliang Cheng;Peichun Li;Beihai Tan;Rong Yu;Yuan Wu;Miao Pan

IEEE Transactions on Cognitive Communications and Networking
Year: 2024 | Early Access Article |
Efficient federated learning (FL) in mobile edge networks faces challenges due to energy-consuming on-device training and wireless transmission. Optimizing the neural network structures is an effective approach to achieving energy savings. In this paper, we present a Snowball FL training with expanding neural network structure, which starts with a small-sized submodel and gradually progresses to a...Show More

Snowball Effect in Federated Learning: An Approach of Exponentially Expanding Structures for Optimizing the Training Efficiency

Guoliang Cheng;Peichun Li;Beihai Tan;Rong Yu;Yuan Wu;Miao Pan

Year: 2024 | Early Access Article |

Coordinated Multi-Point Aided Integrated Sensing, Communication and Computation System: An Energy Efficient Design

Huanyu Dong;Peichun Li;Minghui Dai;Yuan Wu;Liping Qian;Xiaojun Hei

2024 IEEE/CIC International Conference on Communications in China (ICCC)
Year: 2024 | Conference Paper |
With the increasing demand for communication quality, sensing accuracy, and computation latency, the joint design of integrated sensing and communication (ISAC) with mobile edge computing (MEC) systems is becoming an emerging topic in future networks. In this paper, we propose a novel coordinated multi-point (CoMP) aided ISAC-MEC framework. Multiple base stations (BSs) cooperatively conduct target...Show More

Coordinated Multi-Point Aided Integrated Sensing, Communication and Computation System: An Energy Efficient Design

Huanyu Dong;Peichun Li;Minghui Dai;Yuan Wu;Liping Qian;Xiaojun Hei

Year: 2024 | Conference Paper |

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning Over Heterogeneous Mobile Edge Devices

Peichun Li;Hanwen Zhang;Yuan Wu;Liping Qian;Rong Yu;Dusit Niyato;Xuemin Shen

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 10 | Journal Article |
Cited by: Papers (15)
Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices’ resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these c...Show More

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning Over Heterogeneous Mobile Edge Devices

Peichun Li;Hanwen Zhang;Yuan Wu;Liping Qian;Rong Yu;Dusit Niyato;Xuemin Shen

Year: 2024 | Volume: 23, Issue: 10 | Journal Article |

Federated Learning-Empowered AI-Generated Content in Wireless Networks

Xumin Huang;Peichun Li;Hongyang Du;Jiawen Kang;Dusit Niyato;Dong In Kim;Yuan Wu

IEEE Network
Year: 2024 | Volume: 38, Issue: 5 | Magazine Article |
Cited by: Papers (30)
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations...Show More

Federated Learning-Empowered AI-Generated Content in Wireless Networks

Xumin Huang;Peichun Li;Hongyang Du;Jiawen Kang;Dusit Niyato;Dong In Kim;Yuan Wu

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

Quantum-Empowered Federated Learning in Space-Air-Ground Integrated Networks

Tianshun Wang;Peichun Li;Yuan Wu;Liping Qian;Zhou Su;Rongxing Lu

IEEE Network
Year: 2024 | Volume: 38, Issue: 1 | Magazine Article |
Cited by: Papers (4)
As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) has been envisioned to provide numerous intelligent applications that necessitate the cooperation of a multitude of terrestrial devices for machine learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL) offers a promising strategy for distributed training with enhance...Show More

Quantum-Empowered Federated Learning in Space-Air-Ground Integrated Networks

Tianshun Wang;Peichun Li;Yuan Wu;Liping Qian;Zhou Su;Rongxing Lu

Year: 2024 | Volume: 38, Issue: 1 | Magazine Article |

Split Learning with Differential Privacy for Integrated Terrestrial and Non-Terrestrial Networks

Maoqiang Wu;Guoliang Cheng;Peichun Li;Rong Yu;Yuan Wu;Miao Pan;Rongxing Lu

IEEE Wireless Communications
Year: 2024 | Volume: 31, Issue: 3 | Magazine Article |
Cited by: Papers (18)
Integrated terrestrial and non-terrestrial networks (TNTNs) have become a promising architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical applications of TNTNs that collects and analyzes various dimensions of remote sensing data by deploying Internet of Things (IoT) sensors and edge computing in terrestrial, space, aerial, and underwater networks. To improve...Show More

Split Learning with Differential Privacy for Integrated Terrestrial and Non-Terrestrial Networks

Maoqiang Wu;Guoliang Cheng;Peichun Li;Rong Yu;Yuan Wu;Miao Pan;Rongxing Lu

Year: 2024 | Volume: 31, Issue: 3 | Magazine Article |

Camera-Selecting Device-Edge Co-Inference for Real-Time Multi-Camera 3D Pose Estimation

Zhuohang Du;Xumin Huang;Yuan Wu;Pengcheng Tan;Peichun Li;Liping Qian;Haibo Zhou

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
Year: 2023 | Conference Paper |
Multi-camera three-dimensional (3D) pose estimation (MCTPE) has already achieved very high estimation accuracy by utilizing deep neural network (DNN) based models. However, long inference latency of the utilized complex DNN models prevents the real-time deployment of MCTPE. Device-edge collaborative inference (co-inference) is a promising way to reduce the total inference latency of MCTPE, which p...Show More

Camera-Selecting Device-Edge Co-Inference for Real-Time Multi-Camera 3D Pose Estimation

Zhuohang Du;Xumin Huang;Yuan Wu;Pengcheng Tan;Peichun Li;Liping Qian;Haibo Zhou

Year: 2023 | Conference Paper |

FAST: Fidelity-Adjustable Semantic Transmission Over Heterogeneous Wireless Networks

Peichun Li;Guoliang Cheng;Jiawen Kang;Rong Yu;Liping Qian;Yuan Wu;Dusit Niyato

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (7)
In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flex...Show More

FAST: Fidelity-Adjustable Semantic Transmission Over Heterogeneous Wireless Networks

Peichun Li;Guoliang Cheng;Jiawen Kang;Rong Yu;Liping Qian;Yuan Wu;Dusit Niyato

Year: 2023 | Conference Paper |

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
Year: 2023 | Conference Paper |
Cited by: Papers (14)
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model ...Show More

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan

Year: 2023 | Conference Paper |

Snowball: Energy Efficient and Accurate Federated Learning With Coarse-to-Fine Compression Over Heterogeneous Wireless Edge Devices

Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan;Dusit Niyato

IEEE Transactions on Wireless Communications
Year: 2023 | Volume: 22, Issue: 10 | Journal Article |
Cited by: Papers (10)
Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterog...Show More

Snowball: Energy Efficient and Accurate Federated Learning With Coarse-to-Fine Compression Over Heterogeneous Wireless Edge Devices

Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan;Dusit Niyato

Year: 2023 | Volume: 22, Issue: 10 | Journal Article |

FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence

Peichun Li;Yupei Zhong;Chaorui Zhang;Yuan Wu;Rong Yu

IEEE Transactions on Vehicular Technology
Year: 2023 | Volume: 72, Issue: 4 | Journal Article |
Cited by: Papers (7)
Federated learning (FL) is a promising training paradigm to achieve ubiquitous intelligence for future 6G communication systems. However, it is challenging to apply FL in 6G-enabled edge system since decentralized training consumes considerable energy and mobile devices are mostly battery-powered and resource-constrained. The intensive computation and communication cost of local updates accumulate...Show More

FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence

Peichun Li;Yupei Zhong;Chaorui Zhang;Yuan Wu;Rong Yu

Year: 2023 | Volume: 72, Issue: 4 | Journal Article |

Privacy-preserving incentive mechanism for platoon assisted vehicular edge computing with deep reinforcement learning

Xumin Huang;Yupei Zhong;Yuan Wu;Peichun Li;Rong Yu

China Communications
Year: 2022 | Volume: 19, Issue: 7 | Magazine Article |
Cited by: Papers (4)
Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation. In a platoon, a vehicle could play as a requester that employs another vehicles as performers for workload processing. An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making, which avoids the info...Show More

Privacy-preserving incentive mechanism for platoon assisted vehicular edge computing with deep reinforcement learning

Xumin Huang;Yupei Zhong;Yuan Wu;Peichun Li;Rong Yu

Year: 2022 | Volume: 19, Issue: 7 | Magazine Article |

FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

Peichun Li;Xumin Huang;Miao Pan;Rong Yu

2021 IEEE Global Communications Conference (GLOBECOM)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without revealing the local data. Gradient compression could be applied to FL to alleviate the communication overheads but the existing schemes still face challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to control...Show More

FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

Peichun Li;Xumin Huang;Miao Pan;Rong Yu

Year: 2021 | Conference Paper |

FedParking: A Federated Learning Based Parking Space Estimation With Parked Vehicle Assisted Edge Computing

Xumin Huang;Peichun Li;Rong Yu;Yuan Wu;Kan Xie;Shengli Xie

IEEE Transactions on Vehicular Technology
Year: 2021 | Volume: 70, Issue: 9 | Journal Article |
Cited by: Papers (72)
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw dat...Show More

FedParking: A Federated Learning Based Parking Space Estimation With Parked Vehicle Assisted Edge Computing

Xumin Huang;Peichun Li;Rong Yu;Yuan Wu;Kan Xie;Shengli Xie

Year: 2021 | Volume: 70, Issue: 9 | Journal Article |

Toward Resource-Efficient Federated Learning in Mobile Edge Computing

Rong Yu;Peichun Li

IEEE Network
Year: 2021 | Volume: 35, Issue: 1 | Magazine Article |
Cited by: Papers (106)
Federated learning is a newly emerged distributed deep learning paradigm, where the clients separately train their local neural network models with private data and then jointly aggregate a global model at the central server. Mobile edge computing is aimed at deploying mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a prospective distributed fra...Show More

Toward Resource-Efficient Federated Learning in Mobile Edge Computing

Rong Yu;Peichun Li

Year: 2021 | Volume: 35, Issue: 1 | Magazine Article |

Social Welfare Maximization in Container-Based Task Scheduling for Parked Vehicle Edge Computing

Xumin Huang;Peichun Li;Rong Yu

IEEE Communications Letters
Year: 2019 | Volume: 23, Issue: 8 | Journal Article |
Cited by: Papers (30)
Parked vehicle edge computing (PVEC) is proposed to enhance the resource capacity of vehicular networks by utilizing the resources from parked vehicles (PVs). Toward the flexible and fine-grained resource usage, we integrate the container-based virtualization with PVEC for ensuring the task execution in PVs with fast response, increased scalability, and high efficiency. Then we study a social welf...Show More

Social Welfare Maximization in Container-Based Task Scheduling for Parked Vehicle Edge Computing

Xumin Huang;Peichun Li;Rong Yu

Year: 2019 | Volume: 23, Issue: 8 | Journal Article |

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