Zobrazeno 1 - 10
of 967
pro vyhledávání: '"communication efficiency"'
Autor:
Tingting He, Hui Hwang Goh, Weng Kean Yew, Tonni Agustiono Kurniawan, Kai Chen Goh, Quoc-Dung Phan, Shen Yuong Wong
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 12, Pp 103105- (2024)
The coordination of multi-agent systems presents considerable issues in distributed control, particularly in applications like robotic formations, sensor networks, and smart grids. This study addresses the challenge of attaining robust agreement in m
Externí odkaz:
https://doaj.org/article/d0e81cad311246b78e25574f6e1992ec
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This cre
Externí odkaz:
https://doaj.org/article/e5e08ae8cf6b415ab47776d1a3060d27
Autor:
Seyed Mahmoud Sajjadi Mohammadabadi, Mahmoudreza Entezami, Aidin Karimi Moghaddam, Mansour Orangian, Shayan Nejadshamsi
Publikováno v:
International Journal of Intelligent Networks, Vol 5, Iss , Pp 267-274 (2024)
Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data fro
Externí odkaz:
https://doaj.org/article/53f2452b95a64c0b9d61560946f5080e
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 2686-2705 (2024)
Geo-decentralized federated learning (FL) can empower fully distributed model training for future large-scale 6G networks. Without the centralized parameter server, the peer-to-peer model synchronization in geo-decentralized FL would incur excessive
Externí odkaz:
https://doaj.org/article/2cfe6ffee023404e82b4293774e616f8
Publikováno v:
IEEE Access, Vol 12, Pp 57209-57224 (2024)
Advances in Federated Learning and an abundance of user data have enabled rich collaborative learning between multiple clients, without sharing user data. This is done via a central server that aggregates learning in the form of weight updates. Howev
Externí odkaz:
https://doaj.org/article/844838b3fe034063a53fdef1bc65f8d8
Publikováno v:
IEEE Access, Vol 12, Pp 16975-16988 (2024)
Collaborative edge learning has emerged in various domains like vehicular networks and medical care, allowing local model training on edge devices while preserving privacy. The integration of blockchain technology further enhances security and privac
Externí odkaz:
https://doaj.org/article/9c115945c43b4a029f3756cf1116ed52
Publikováno v:
Mathematics, Vol 12, Iss 18, p 2932 (2024)
With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are red
Externí odkaz:
https://doaj.org/article/8045b44621e845e5b329869aa7410b8f
Publikováno v:
Brain Sciences, Vol 14, Iss 8, p 809 (2024)
Background: The purpose of this study was to explore the specific regions of abnormal cortical communication efficiency in patients with mild subcortical stroke and to investigate the relationship between these communication efficiency abnormalities
Externí odkaz:
https://doaj.org/article/4f303582693340a98c5a07b9e5d74fa1
Autor:
Sajjadi Mohammadabadi, Seyed Mahmoud a, ⁎, Entezami, Mahmoudreza b, Karimi Moghaddam, Aidin c, Orangian, Mansour d, Nejadshamsi, Shayan e
Publikováno v:
In International Journal of Intelligent Networks 2024 5:267-274
Autor:
Xie Yuan
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The intersection of technology and tradition offers unprecedented opportunities for spreading folk culture, mainly through innovative knowledge graph technology. This research delves into the transformative potential of knowledge mapping for folk cul
Externí odkaz:
https://doaj.org/article/b8b5eb56b7734562aebc66f72e54f47f