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Found 7 papers, 0 papers with code
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks.
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach
As a promising paradigm federated Learning (FL) is widely used in privacy-preserving machine learning, which allows distributed devices to collaboratively train a model while avoiding data transmission among clients.
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques.
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication
Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication.
Collision Avoidance
Explainable Artificial Intelligence (XAI)
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
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 select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic.
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT.
