7 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
Ruijin Sun, Xiao Yang, Nan Cheng +2
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation bu…
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
Longfei Ma, Nan Cheng, Xiucheng Wang +3
As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-…
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Xiucheng Wang, Nan Cheng, Longfei Ma +3
In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can se…
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
Xiucheng Wang, Longfei Ma, Haocheng Li +3
Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmi…