Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Ding, Jiahao"'
Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involv
Externí odkaz:
http://arxiv.org/abs/2301.01838
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed
Externí odkaz:
http://arxiv.org/abs/2201.12515
Autor:
Zhang, Yi, Wang, San-Wang, Ding, Jiahao, Wen, Xin, Li, Tingting, Yang, Lu, Peng, Jintao, Dong, Yingying, Mi, Weifeng, Gao, Yujun, Sun, Guizhi
Publikováno v:
In Journal of Affective Disorders 15 September 2024 361:165-171
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their o
Externí odkaz:
http://arxiv.org/abs/2111.00637
Autor:
Wang, Kehui, Yang, Xiangjun, Liang, Junjun, Rong, Yan, Zhao, Weijie, Ding, Jiahao, Liu, Yiming, Liu, Qi
Publikováno v:
In International Journal of Biological Macromolecules October 2024 277 Part 4
Publikováno v:
In Tribology International April 2024 192
Autor:
Wu, Maoqiang, Zhang, Xinyue, Ding, Jiahao, Nguyen, Hien, Yu, Rong, Pan, Miao, Wong, Stephen T.
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained for medical applications. Recently developed inference attack algorithm
Externí odkaz:
http://arxiv.org/abs/2011.00177
(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sen
Externí odkaz:
http://arxiv.org/abs/2010.13520
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential roles in sparse learning, and have drawn extensive attentions recently.
Externí odkaz:
http://arxiv.org/abs/2009.06562
The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy c
Externí odkaz:
http://arxiv.org/abs/2008.04500