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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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Wenbo Zhang, Yuchen Zhao, Fangjing Li and Hongbo Zhu
Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information ...
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Riccardo Lazzarini, Huaglory Tianfield and Vassilis Charissis
The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDS...
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Liangkun Yu, Xiang Sun, Rana Albelaihi and Chen Yi
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the co...
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Aristeidis Karras, Christos Karras, Konstantinos C. Giotopoulos, Dimitrios Tsolis, Konstantinos Oikonomou and Spyros Sioutas
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent chall...
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Sumit Rai, Arti Kumari and Dilip K. Prasad
Federated learning promises an elegant solution for learning global models across distributed and privacy-protected datasets. However, challenges related to skewed data distribution, limited computational and communication resources, data poisoning, and ...
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Ahmed A. Al-Saedi, Veselka Boeva and Emiliano Casalicchio
Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To ad...
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