Zobrazeno 1 - 10
of 477
pro vyhledávání: '"Ding Yucheng"'
Publikováno v:
E3S Web of Conferences, Vol 441, p 01008 (2023)
With the release and gradual implementation of the “carbon peaking and carbon neutrality” policy, renewable energy is integrated into power grids on a large scale, and its uncertain and intermittent supply challenges the stability of the power sy
Externí odkaz:
https://doaj.org/article/cde2a119f27847b0b54ab1c905310fef
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage the device-side samples to improve the cloud-side large model becomes a
Externí odkaz:
http://arxiv.org/abs/2303.10361
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the
Externí odkaz:
http://arxiv.org/abs/2211.01163
Publikováno v:
In Chemosphere October 2024 365
Autor:
Ding, Yucheng, Niu, Chaoyue, Wu, Fan, Tang, Shaojie, Lv, Chengfei, Feng, Yanghe, Chen, Guihai
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves only a small part of the full model, called a submodel. Due to data sp
Externí odkaz:
http://arxiv.org/abs/2109.07704
Autor:
Qiu, Xiao, Ding, Yucheng, Sun, Zhibo, Ji, Haocheng, Zhou, Yu, Long, Zhenghao, Liu, Gongze, Wang, Peiyao, Poddar, Swapnadeep, Ren, Beitao, Zhou, Kemeng, Li, Ziyun, Cao, Yang Bryan, Ma, Zichao, Li, Baikui, Lin, Yuanjing, Huang, Baoling, Wang, Jiannong, Kwok, Hoi Sing, Fan, Zhiyong
Publikováno v:
In Device 17 May 2024 2(5)
Autor:
Ding, Yucheng, Niu, Chaoyue, Yan, Yikai, Zheng, Zhenzhe, Wu, Fan, Chen, Guihai, Tang, Shaojie, Jia, Rongfei
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains several blocks, and each client's training data follow block-specific and n
Externí odkaz:
http://arxiv.org/abs/2002.07454
Autor:
Yan, Yikai, Niu, Chaoyue, Ding, Yucheng, Zheng, Zhenzhe, Wu, Fan, Chen, Guihai, Tang, Shaojie, Wu, Zhihua
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federate
Externí odkaz:
http://arxiv.org/abs/2002.07399
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