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pro vyhledávání: '"FAN QIANG"'
In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimi
Externí odkaz:
http://arxiv.org/abs/2412.13204
This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is
Externí odkaz:
http://arxiv.org/abs/2411.04672
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of c
Externí odkaz:
http://arxiv.org/abs/2410.07881
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significan
Externí odkaz:
http://arxiv.org/abs/2408.09194
Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RS
Externí odkaz:
http://arxiv.org/abs/2408.00256
Autor:
Zhang, Cui, Zhang, Wenjun, Wu, Qiong, Fan, Pingyi, Fan, Qiang, Wang, Jiangzhou, Letaief, Khaled B.
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually lar
Externí odkaz:
http://arxiv.org/abs/2407.08462
Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle
Externí odkaz:
http://arxiv.org/abs/2407.08458
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications a
Externí odkaz:
http://arxiv.org/abs/2406.11318
This letter proposes a semantic-aware resource allocation (SARA) framework with flexible duty cycle (DC) coexistence mechanism (SARADC) for 5G-V2X Heterogeneous Network (HetNets) based on deep reinforcement learning (DRL) proximal policy optimization
Externí odkaz:
http://arxiv.org/abs/2406.07996
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing c
Externí odkaz:
http://arxiv.org/abs/2404.08444