Zobrazeno 1 - 10
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pro vyhledávání: '"Lam Chan"'
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on e
Externí odkaz:
http://arxiv.org/abs/2412.07197
We first present a simple recursive algorithm that generates cyclic rotation Gray codes for stamp foldings and semi-meanders, where consecutive strings differ by a stamp rotation. These are the first known Gray codes for stamp foldings and semi-meand
Externí odkaz:
http://arxiv.org/abs/2411.05458
In recent years, the rapid evolution of satellite communications play a pivotal role in addressing the ever-increasing demand for global connectivity, among which the Low Earth Orbit (LEO) satellites attract a great amount of attention due to their l
Externí odkaz:
http://arxiv.org/abs/2409.17553
A novel wireless transmission scheme, as named the reconfigurable intelligent surface (RIS)-assisted received adaptive spatial modulation (RASM) scheme, is proposed in this paper. In this scheme, the adaptive spatial modulation (ASM)-based antennas s
Externí odkaz:
http://arxiv.org/abs/2407.06894
In this work, we present a tutorial on how to account for the computational time complexity overhead of signal processing in the spectral efficiency (SE) analysis of wireless waveforms. Our methodology is particularly relevant in scenarios where achi
Externí odkaz:
http://arxiv.org/abs/2407.05805
The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the m
Externí odkaz:
http://arxiv.org/abs/2403.08989
Autor:
Chong, Chak Fong, Fang, Xinyi, Guo, Jielong, Wang, Yapeng, Ke, Wei, Lam, Chan-Tong, Im, Sio-Kei
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image. Assigning pseudo-labels to unknown labels to gain additional training signals has become prevalent for training deep classification
Externí odkaz:
http://arxiv.org/abs/2401.16991
Autor:
Xiong, Xiangyu, Sun, Yue, Liu, Xiaohong, Ke, Wei, Lam, Chan-Tong, Chen, Jiangang, Jiang, Mingfeng, Wang, Mingwei, Xie, Hui, Tong, Tong, Gao, Qinquan, Chen, Hao, Tan, Tao
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generat
Externí odkaz:
http://arxiv.org/abs/2312.17538
Autor:
Xiong, Xiangyu, Sun, Yue, Liu, Xiaohong, Lam, Chan-Tong, Tong, Tong, Chen, Hao, Gao, Qinquan, Ke, Wei, Tan, Tao
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularl
Externí odkaz:
http://arxiv.org/abs/2311.14388
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during
Externí odkaz:
https://doaj.org/article/6c517475813a4f608a567fb3da30890f
