Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Xiao, Yonghui"'
Federated learning (FL) has shown promising results on training machine learning models with privacy preservation. However, for large models with over 100 million parameters, the training resource requirement becomes an obstacle for FL because common
Externí odkaz:
http://arxiv.org/abs/2408.10443
This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy. We employ federated learning and parameter-efficient domain adaptation methods
Externí odkaz:
http://arxiv.org/abs/2408.11873
Autor:
Lin, Rongmei, Xiao, Yonghui, Yang, Tien-Ju, Zhao, Ding, Xiong, Li, Motta, Giovanni, Beaufays, Françoise
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been widely used and is considered to be an effective decentralized techniqu
Externí odkaz:
http://arxiv.org/abs/2209.06359
Autor:
Yang, Tien-Ju, Xiao, Yonghui, Motta, Giovanni, Beaufays, Françoise, Mathews, Rajiv, Chen, Mingqing
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Online Model Compression (OMC) provides a framework that stores model para
Externí odkaz:
http://arxiv.org/abs/2205.03494
Autor:
Wang, Wei, Pan, Yishan, Xiao, Yonghui, Dai, Lianpeng, Zhang, Xinping, Wang, Yuheng, Qin, Xufeng, Zhu, Yanfei, Liu, Yan, Li, Gang
Publikováno v:
In Journal of Rock Mechanics and Geotechnical Engineering October 2024 16(10):3885-3906
Autor:
Guliani, Dhruv, Zhou, Lillian, Ryu, Changwan, Yang, Tien-Ju, Zhang, Harry, Xiao, Yonghui, Beaufays, Francoise, Motta, Giovanni
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents a challenge pertaining to the communication and computation costs associated with clients'
Externí odkaz:
http://arxiv.org/abs/2110.03634
Autor:
Zhang, Jianzhuo, Guo, Hao, Xiao, Yonghui, Pan, Yishan, Guo, Chenguang, Ni, Baojun, Wang, Shuwen
Publikováno v:
In Alexandria Engineering Journal July 2024 98:266-280
Autor:
Wang, Wei, Zhao, Like, Pan, Yishan, Li, Hongbin, Luo, Hao, Zhang, Xueqi, Xiao, Yonghui, Hu, Shuwei, Li, Xiaoliang
Publikováno v:
In Journal of Rock Mechanics and Geotechnical Engineering August 2024
Publikováno v:
In Journal of Rock Mechanics and Geotechnical Engineering January 2024 16(1):1-25
Autor:
Cao, Yang, Xiao, Yonghui, Takagi, Shun, Xiong, Li, Yoshikawa, Masatoshi, Shen, Yilin, Liu, Jinfei, Jin, Hongxia, Xu, Xiaofeng
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off between privacy and utility in many real-world applications. To address
Externí odkaz:
http://arxiv.org/abs/2005.01263