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João P. Ferreira, Vinicius C. Ferreira, Sérgio L. Nogueira, João M. Faria and José A. Afonso
The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and netw...
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Ying-Hsun Lai, Shin-Yeh Chen, Wen-Chi Chou, Hua-Yang Hsu and Han-Chieh Chao
Federated learning trains a neural network model using the client?s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) becau...
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David Naseh, Mahdi Abdollahpour and Daniele Tarchi
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed in...
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Gerardo Hernández-Oregón, Mario E. Rivero-Angeles, Juan C. Chimal-Eguía and Jorge E. Coyac-Torres
Peer-to-Peer (P2P) networks have emerged as potential solutions to issues that cause inefficient download times in networks because they can use the resources in the entire network, allowing nodes to act both as servers and clients simultaneously. Common...
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Mohamed Chetoui and Moulay A. Akhloufi
The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. ...
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Shunji Aoyagi, Yuki Horie, Do Thi Thu Hien, Thanh Duc Ngo, Duy-Dinh Le, Kien Nguyen and Hiroo Sekiya
An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. I...
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Muneerah Al Asqah and Tarek Moulahi
The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious ...
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Luzhi Li, Yuhong Zhao, Jingyu Wang and Chuanting Zhang
Wireless traffic prediction is critical to the intelligent operation of cellular networks, such as load balancing, congestion control, value-added service promotion, etc. However, the BTS data in each region has certain differences and privacy, and centr...
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Christian Moya and Guang Lin
The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized ...
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Matin Mortaheb, Cemil Vahapoglu and Sennur Ulukus
Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achi...
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