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pro vyhledávání: '"Wu Hongda"'
Federated Learning (FL) enables many resource-limited devices to train a model collaboratively without data sharing. However, many existing works focus on model-homogeneous FL, where the global and local models are the same size, ignoring the inheren
Externí odkaz:
http://arxiv.org/abs/2311.10002
The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks of these networks. Besides, minimum d
Externí odkaz:
http://arxiv.org/abs/2203.12674
The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However, most of the existing polar-coded SCMA (PC-SCMA) systems suffer from hi
Externí odkaz:
http://arxiv.org/abs/2110.09977
Autor:
Wu, Hongda, Wang, Ping
Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke d
Externí odkaz:
http://arxiv.org/abs/2105.07066
Publikováno v:
In Applied Surface Science 15 March 2024 649
Autor:
Wu, Hongda, Wang, Ping
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID
Externí odkaz:
http://arxiv.org/abs/2012.00661
Publikováno v:
In Ocean Engineering 1 December 2023 289 Part 1
Boosting sodium-ion storage performance by tailoring intragranular porous WS2/C nanocomposites anode
Publikováno v:
In Applied Surface Science 15 April 2023 616
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