Zobrazeno 1 - 10
of 3 472
pro vyhledávání: '"Hu , Miao"'
Autor:
Zhang, Xianzhi, Zhou, Yipeng, Hu, Miao, Wu, Di, Liao, Pengshan, Guizani, Mohsen, Sheng, Michael
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private feder
Externí odkaz:
http://arxiv.org/abs/2412.02934
In the era of big data, managing evolving graph data poses substantial challenges due to storage costs and privacy issues. Training graph neural networks (GNNs) on such evolving data usually causes catastrophic forgetting, impairing performance on ea
Externí odkaz:
http://arxiv.org/abs/2411.18919
Autor:
Wang, Zihan, Yang, Daniel W., Liu, Zerui, Yan, Evan, Sun, Heming, Ge, Ning, Hu, Miao, Wu, Wei
This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to e
Externí odkaz:
http://arxiv.org/abs/2410.14882
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are autom
Externí odkaz:
http://arxiv.org/abs/2408.14735
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations
Externí odkaz:
http://arxiv.org/abs/2405.01844
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfortunately, a significant challenge of subgraph-FL arises from subgraph
Externí odkaz:
http://arxiv.org/abs/2404.14061
Autor:
Zhang, Xianzhi, Xiao, Linchang, Zhou, Yipeng, Hu, Miao, Wu, Di, Lui, John C. S., Sheng, Quan Z.
As users conveniently stream their favorite online videos, video request records are automatically stored by video content providers, which have a high chance of privacy leakage. Unfortunately, most existing privacy-enhancing approaches are not appli
Externí odkaz:
http://arxiv.org/abs/2310.12622
Autor:
MENG Weimin, GAO Yaxin, HU Miao, WEN Wei, ZHANG Pengfei, ZHANG Fengxia, WANG Fengzhong, LI Shuying
Publikováno v:
Shipin Kexue, Vol 45, Iss 22, Pp 280-290 (2024)
Okara, the major by-product from the production of soybean products, is rich in dietary fiber, protein, fat, vitamins and other nutrients, and functions to regulate the intestinal flora, prevent diabetes, control body mass, prevent and treat cardiova
Externí odkaz:
https://doaj.org/article/43f19fd67fd84793b85836761871ff45
Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregati
Externí odkaz:
http://arxiv.org/abs/2305.06124
Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a par
Externí odkaz:
http://arxiv.org/abs/2305.05221