9 citations · 14 across the 6 of their papers we have counts for
10 papers
When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
Qun Wang, Haijian Sun, Rose Qingyang Hu +1
The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. C…
User Scheduling for Federated Learning Through Over-the-Air Computation
Xiang Ma, Haijian Sun, Qun Wang +1
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.…
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
Qun Wang, Han Hu, Haijian Sun +1
Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existi…
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Xiang Ma, Haijian Sun, Rose Qingyang Hu
Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive…
Towards Green Mobile Edge Computing Offloading Systems with Security Enhancement
Haijian Sun, Qun Wang, Xiang Ma +2
Mobile edge computing (MEC) is an emerging communication scheme that aims at reducing latency. In this paper, we investigate a green MEC system under the existence of an eavesdropp…
Adaptive Federated Learning With Gradient Compression in Uplink NOMA
Haijian Sun, Xiang Ma, Rose Qingyang Hu
Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices. Several unique features such as energ…