Zobrazeno 1 - 10
of 551
pro vyhledávání: '"Li Anran"'
Autor:
Li, Anran, Chen, Yuanyuan, Ren, Chao, Wang, Wenhan, Hu, Ming, Li, Tianlin, Yu, Han, Chen, Qingyu
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is scaling to large-scale graphs, which typically incurs high computation an
Externí odkaz:
http://arxiv.org/abs/2409.14655
Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due
Externí odkaz:
http://arxiv.org/abs/2407.12729
Autor:
Ren, Chao, Yu, Han, Peng, Hongyi, Tang, Xiaoli, Zhao, Bo, Yi, Liping, Tan, Alysa Ziying, Gao, Yulan, Li, Anran, Li, Xiaoxiao, Li, Zengxiang, Yang, Qiang
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and
Externí odkaz:
http://arxiv.org/abs/2404.15381
Autor:
Xia, Zeke, Hu, Ming, Yan, Dengke, Xie, Xiaofei, Li, Tianlin, Li, Anran, Zhou, Junlong, Chen, Mingsong
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the pres
Externí odkaz:
http://arxiv.org/abs/2404.12850
Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the neglect of data heterogeneity and catastrophic forgetting. To address th
Externí odkaz:
http://arxiv.org/abs/2404.12846
Recently, Automated Vulnerability Localization (AVL) has attracted much attention, aiming to facilitate diagnosis by pinpointing the lines of code responsible for discovered vulnerabilities. Large Language Models (LLMs) have shown potential in variou
Externí odkaz:
http://arxiv.org/abs/2404.00287
Autor:
Li Anran, Wei Chongqing
Publikováno v:
Advances in Nonlinear Analysis, Vol 7, Iss 4, Pp 485-496 (2018)
In this paper, we deal with fractional p-Laplacian equations of the form
Externí odkaz:
https://doaj.org/article/e354eae2b89046b8acc5a3bdb4ba802e
Autor:
Ewen, Parker, Chen, Hao, Chen, Yuzhen, Li, Anran, Bagali, Anup, Gunjal, Gitesh, Vasudevan, Ram
Robots must be able to understand their surroundings to perform complex tasks in challenging environments and many of these complex tasks require estimates of physical properties such as friction or weight. Estimating such properties using learning i
Externí odkaz:
http://arxiv.org/abs/2402.05872
Players cooperating in a line is a special while essential phenomenon in real life collaborating activities such as assembly line production, pipeline supply chain management and other streamlining operational settings. In this paper, we study the sc
Externí odkaz:
http://arxiv.org/abs/2309.13300
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG d
Externí odkaz:
http://arxiv.org/abs/2308.11636