47 citations · 87 across the 13 of their papers we have counts for
6 papers · 1 filter
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
Li Dong, Yubo Peng, Feibo Jiang +2
In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hen…
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
Yubo Peng, Feibo Jiang, Li Dong +2
Federated learning (FL) is a commonly distributed algorithm for mobile users (MUs) training artificial intelligence (AI) models, however, several challenges arise when applying FL…
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
Yubo Peng, Feibo Jiang, Li Dong +2
Semantic Communication (SC) focuses on transmitting only the semantic information rather than the raw data. This approach offers an efficient solution to the issue of spectrum reso…
Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
Li Dong, Feibo Jiang, Minjie Wang +2
The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal…
Personalized Wireless Federated Learning for Large Language Models
Feibo Jiang, Li Dong, Siwei Tu +5
Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges rel…
Large Generative Model Assisted 3D Semantic Communication
Feibo Jiang, Yubo Peng, Li Dong +4
Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extracti…