Zobrazeno 1 - 10
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pro vyhledávání: '"Ma, Longfei"'
Autor:
Ma, Longfei, Cheng, Nan, Wang, Xiucheng, Chen, Jiong, Gao, Yinjun, Zhang, Dongxiao, Zhang, Jun-Jie
The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict t
Externí odkaz:
http://arxiv.org/abs/2406.13145
Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying
Externí odkaz:
http://arxiv.org/abs/2310.16302
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. Despite its immense potential, th
Externí odkaz:
http://arxiv.org/abs/2308.13849
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-based optimization methods achieve strong performance, the high comple
Externí odkaz:
http://arxiv.org/abs/2308.07511
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. As a result, UAVs have been extensively used as aerial ba
Externí odkaz:
http://arxiv.org/abs/2307.02002
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 b
Externí odkaz:
http://arxiv.org/abs/2303.06155
Publikováno v:
Scientific Reports. 9/27/2024, Vol. 14 Issue 1, p1-11. 11p.
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. In this
Externí odkaz:
http://arxiv.org/abs/2208.01785
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 transmitted to server at a time, while server processes tasks according to th
Externí odkaz:
http://arxiv.org/abs/2208.01781
Publikováno v:
In Materials Science in Semiconductor Processing October 2024 181