590 citations · 1.9k across the 75 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…
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
Yao Wen, Guopeng Zhang, Kezhi Wang +1
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learni…
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets
Na Yan, Kezhi Wang, Cunhua Pan +3
To jointly overcome the communication bottleneck and privacy leakage of wireless federated learning (FL), this paper studies a differentially private over-the-air federated averagi…
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities
Muhammad Khalid, Liang Wang, Kezhi Wang +3
In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAV…
Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model
Jeyamohan Neera, Xiaomin Chen, Nauman Aslam +2
Recommendation systems rely heavily on users behavioural and preferential data (e.g. ratings, likes) to produce accurate recommendations. However, users experience privacy concerns…
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
Feibo Jiang, Kezhi Wang, Li Dong +2
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, t…