Zobrazeno 1 - 10
of 577
pro vyhledávání: '"Yang, Howard"'
Autor:
Li, Jiaxing, Chen, Zihan, Chong, Kai Fong Ernest, Das, Bikramjit, Quek, Tony Q. S., Yang, Howard H.
Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superposition properties of multi-access channels, this approach facilitates a
Externí odkaz:
http://arxiv.org/abs/2409.15100
Autor:
Chen, Jiao, He, Jiayi, Chen, Fangfang, Lv, Zuohong, Tang, Jianhua, Li, Weihua, Liu, Zuozhu, Yang, Howard H., Han, Guangjie
Currently, most applications in the Industrial Internet of Things (IIoT) still rely on CNN-based neural networks. Although Transformer-based large models (LMs), including language, vision, and multimodal models, have demonstrated impressive capabilit
Externí odkaz:
http://arxiv.org/abs/2409.01207
Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. However, existing ISC systems based on joint source-channel coding face challenges in interpretability,
Externí odkaz:
http://arxiv.org/abs/2408.03806
Autor:
Chen, Zhengchuan, Lang, Kang, Pappas, Nikolaos, Yang, Howard H., Wang, Min, Tian, Zhong, Quek, Tony Q. S.
Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parall
Externí odkaz:
http://arxiv.org/abs/2405.16965
In this paper, we introduce a novel mathematical framework for assessing the performance of joint communication and sensing (JCAS) in wireless networks, employing stochastic geometry as an analytical tool. We focus on deriving the meta distribution o
Externí odkaz:
http://arxiv.org/abs/2404.01672
Autor:
Wang, Chenhao, Chen, Zihan, Pappas, Nikolaos, Yang, Howard H., Quek, Tony Q. S., Poor, H. Vincent
We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the inherent superposition property of wireless channels, facilitating fast
Externí odkaz:
http://arxiv.org/abs/2403.06528
We leverage the Multiplicative Weight Update (MWU) method to develop a decentralized algorithm that significantly improves the performance of dynamic time division duplexing (D-TDD) in small cell networks. The proposed algorithm adaptively adjusts th
Externí odkaz:
http://arxiv.org/abs/2402.05641
Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks
Externí odkaz:
http://arxiv.org/abs/2402.05407
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously
Externí odkaz:
http://arxiv.org/abs/2401.17124
Autor:
Xiao, Zikai, Chen, Zihan, Liu, Liyinglan, Feng, Yang, Wu, Jian, Liu, Wanlu, Zhou, Joey Tianyi, Yang, Howard Hao, Liu, Zuozhu
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times. In the context of Fed-LT, existi
Externí odkaz:
http://arxiv.org/abs/2401.08977