Zobrazeno 1 - 10
of 431
pro vyhledávání: '"Yuan, Honglin"'
Autor:
Yuan, Honglin
Federated Learning (FL), a distributed learning paradigm that scales on-device learning collaboratively, has emerged as a promising approach for decentralized AI applications. Local optimization methods such as Federated Averaging (FedAvg) are the mo
Externí odkaz:
http://arxiv.org/abs/2401.13216
Autor:
Tran, Dustin, Liu, Jeremiah, Dusenberry, Michael W., Phan, Du, Collier, Mark, Ren, Jie, Han, Kehang, Wang, Zi, Mariet, Zelda, Hu, Huiyi, Band, Neil, Rudner, Tim G. J., Singhal, Karan, Nado, Zachary, van Amersfoort, Joost, Kirsch, Andreas, Jenatton, Rodolphe, Thain, Nithum, Yuan, Honglin, Buchanan, Kelly, Murphy, Kevin, Sculley, D., Gal, Yarin, Ghahramani, Zoubin, Snoek, Jasper, Lakshminarayanan, Balaji
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical t
Externí odkaz:
http://arxiv.org/abs/2207.07411
Publikováno v:
In Ore Geology Reviews September 2024 172
Federated Averaging (FedAvg), also known as Local SGD, is one of the most popular algorithms in Federated Learning (FL). Despite its simplicity and popularity, the convergence rate of FedAvg has thus far been undetermined. Even under the simplest ass
Externí odkaz:
http://arxiv.org/abs/2111.03741
Autor:
Kelner, Jonathan, Marsden, Annie, Sharan, Vatsal, Sidford, Aaron, Valiant, Gregory, Yuan, Honglin
We provide new gradient-based methods for efficiently solving a broad class of ill-conditioned optimization problems. We consider the problem of minimizing a function $f : \mathbb{R}^d \rightarrow \mathbb{R}$ which is implicitly decomposable as the s
Externí odkaz:
http://arxiv.org/abs/2111.03137
Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance g
Externí odkaz:
http://arxiv.org/abs/2110.14216
Autor:
Wang, Jianyu, Charles, Zachary, Xu, Zheng, Joshi, Gauri, McMahan, H. Brendan, Arcas, Blaise Aguera y, Al-Shedivat, Maruan, Andrew, Galen, Avestimehr, Salman, Daly, Katharine, Data, Deepesh, Diggavi, Suhas, Eichner, Hubert, Gadhikar, Advait, Garrett, Zachary, Girgis, Antonious M., Hanzely, Filip, Hard, Andrew, He, Chaoyang, Horvath, Samuel, Huo, Zhouyuan, Ingerman, Alex, Jaggi, Martin, Javidi, Tara, Kairouz, Peter, Kale, Satyen, Karimireddy, Sai Praneeth, Konecny, Jakub, Koyejo, Sanmi, Li, Tian, Liu, Luyang, Mohri, Mehryar, Qi, Hang, Reddi, Sashank J., Richtarik, Peter, Singhal, Karan, Smith, Virginia, Soltanolkotabi, Mahdi, Song, Weikang, Suresh, Ananda Theertha, Stich, Sebastian U., Talwalkar, Ameet, Wang, Hongyi, Woodworth, Blake, Wu, Shanshan, Yu, Felix X., Yuan, Honglin, Zaheer, Manzil, Zhang, Mi, Zhang, Tong, Zheng, Chunxiang, Zhu, Chen, Zhu, Wennan
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving f
Externí odkaz:
http://arxiv.org/abs/2107.06917
An estimation method of Radio Frequency fingerprint (RFF) based on the physical hardware properties of the nonlinearity and in-phase and quadrature (IQ) imbalance of the transmitter is proposed for the authentication of wireless orthogonal frequency
Externí odkaz:
http://arxiv.org/abs/2104.10397
Autor:
Jiang, Chengyao, Liu, Peng, Gleeson, Sarah A., Bao, Zhian, Li, Chao, Mathur, Ryan, Ouyang, Yongpeng, Lv, Nan, Mao, Jingwen, Yuan, Honglin
Publikováno v:
In Ore Geology Reviews January 2024 164
Publikováno v:
In International Journal of Thermal Sciences January 2024 195