Jun Yang Li - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Jun Yang Li

Publications

1

Citations

21

Publications by Year

20212021

Co-Authors:

Aaron M. JohnsonJoseph NorbyAmir PatelCameron Selby

Jun Yang Li

Affiliation

Department of Mechanical Engineering

Carnegie Mellon University

Pittsburgh, PA, USA

Publication Topics

Actuator,
Actuator Model,
Aerodynamic Forces,
Agility,
Angular Momentum,
Angular Velocity,
Body Angle,
Body Length,
Body Rotation,
Body Velocity,
Carbon Fiber,
Cheetah

Biography

Jun Yang Li received the B.S. and M.S. degrees in mechanical engineering in 2019 from Carnegie Mellon University, Pittsburgh, PA, USA, in 2018 and 2019, respectively.,From September 2018 to May 2019, he was a Research Assistant with the Robomechanics Lab, Carnegie Mellon University. From 2019 to 2020, he was a Mechanical Designer Intern with the HG Tech, Wuhan, China.(Based on document published on 6 January 2021).

Publications

1

Citations

21

Publications by Year

20212021

Co-Authors:

Aaron M. Johnson
Joseph Norby
Amir Patel
Cameron Selby

Author's Published Works

Search History

Showing 1-9 of 9 results

Conferences (4)

Journals (3)

Early Access Articles (1)

Magazines (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Guoliang Cheng(9)
Rong Yu(8)
Yuan Wu(6)
Miao Pan(6)
Peichun Li(5)
School of Automation, Guangdong University of Technology, Guangzhou, China(8)
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China(4)
Guangdong Key Laboratory of IoT Information Technology, Guangzhou, China(3)
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA(3)
Department of Electrical and Computer Engineering, University of Houston, Houston, USA(2)
2021 16th International Conference on Computer Science & Education (ICCSE)(1)
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(1)
ICC 2023 - IEEE International Conference on Communications(1)
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications(1)
IEEE Transactions on Cognitive Communications and Networking(1)
IEEE(9)
Bilbao, Spain(1)
Lancaster, United Kingdom(1)
New York City, NY, USA(1)
Rome, Italy(1)
Federated Learning(5)
Energy Consumption(4)
Global Model(3)
Information Asymmetry(3)
Optimization Problem(3)

Select All on Page

Sort By

Results

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

IEEE Transactions on Cognitive Communications and Networking

Year: 2024 | Early Access Article |

Multi-Dimensional Contract Design for Energy-Efficient Wireless Sensing in Consumer-Centric E-Commerce Systems

Dongdong Ye;Xumin Huang;Guoliang Cheng;M. Shamim Hossain

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 4 | Journal Article |

HTML

E-commerce systems employ Consumer Internet of Things (CIoT) devices, like voice assistants, wearables, and smart plugs, to sense and collect massive data on commodities and consumer behavior. This data is then used to analyze e-commerce patterns, predict consumer behavior, and provide recommendations for consumer-centric e-commerce services. However, this leads to high energy consumption for the ...Show More

Multi-Dimensional Contract Design for Energy-Efficient Wireless Sensing in Consumer-Centric E-Commerce Systems

Dongdong Ye;Xumin Huang;Guoliang Cheng;M. Shamim Hossain

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 4 | Journal Article |

Federated Split Learning With Data and Label Privacy Preservation in Vehicular Networks

Maoqiang Wu;Guoliang Cheng;Dongdong Ye;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 1 | Journal Article |

Cited by: Papers (6)

HTML

Federatedlearning (FL) is an emerging distributed learning paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for the server while keeping private data locally. However, the annotation of private data by vehicular users is very difficult since the high costs and professional needs, and one solution is that roadside infrastructures could provide label map...Show More

Federated Split Learning With Data and Label Privacy Preservation in Vehicular Networks

Maoqiang Wu;Guoliang Cheng;Dongdong Ye;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 1 | Journal 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 (17)

HTML

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

IEEE Wireless Communications

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

Resting Your Car for Cash: Incentive Mechanism Based TNC Car Scheduling for Carbon Neutrality

Maoqiang Wu;Guoliang Cheng;Pavana Prakash;Rong Yu;Xiaoming Xiong;Miao Pan

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

HTML

The transportation network company (TNC) services (i.e., Uber, Lyft, Didi, etc.) can effectively match the vehicles/drivers with passengers via mobile applications, and solve the first- and last-mile problem. However, most idle TNC vehicles have to travel around looking for the next order, which causes unnecessary carbon emissions. In addition, the idle TNC vehicles tend to move to the higher-trip...Show More

Resting Your Car for Cash: Incentive Mechanism Based TNC Car Scheduling for Carbon Neutrality

Maoqiang Wu;Guoliang Cheng;Pavana Prakash;Rong Yu;Xiaoming Xiong;Miao Pan

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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)

HTML

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

ICC 2023 - IEEE International Conference on Communications

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)

HTML

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

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications

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)

HTML

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

IEEE Transactions on Wireless Communications

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

Online Course Construction and Multi-dimensional Mixed Teaching: Research and Practice

Beihai Tan;Guoliang Cheng;Rong Yu;Shuting Cai

2021 16th International Conference on Computer Science & Education (ICCSE)

Year: 2021 | Conference Paper |

HTML

Online course is a promising education paradigm to offer easy-to-access knowledge for everyone. However, the overemphasis on online education may lead to the fragmentation of knowledge and distract attention away from the on-campus courses for the student. To tackle this dilemma, we investigate the Flipped Classroom teaching model by combing the advantages of online and on-campus courses in this p...Show More

Online Course Construction and Multi-dimensional Mixed Teaching: Research and Practice

Beihai Tan;Guoliang Cheng;Rong Yu;Shuting Cai

2021 16th International Conference on Computer Science & Education (ICCSE)

Year: 2021 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.