116 citations · 185 across the 6 of their papers we have counts for
6 papers
Federated Learning-Empowered AI-Generated Content in Wireless Networks
Xumin Huang, Peichun Li, Hongyang Du +4
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process…
FAST: Fidelity-Adjustable Semantic Transmission over Heterogeneous Wireless Networks
Peichun Li, Guoliang Cheng, Jiawen Kang +4
In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmissio…
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li, Guoliang Cheng, Xumin Huang +4
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-adjus…
FedBA: Non-IID Federated Learning Framework in UAV Networks
Pei Li, Zhijun Liu, Luyi Chang +2
With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving…
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing
Peichun Li, Xumin Huang, Miao Pan +1
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied…
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
Xumin Huang, Peichun Li, Rong Yu +3
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the applicatio…