no code implementations • 6 Sep 2023 • Xinghua Zhu, Zhixin Liu
In this paper, we study the distributed adaptive estimation problem of continuous-time stochastic dynamic systems over sensor networks where each agent can only communicate with its local neighbors.
no code implementations • 10 Jul 2021 • Shijing Si, Jianzong Wang, Xiaoyang Qu, Ning Cheng, Wenqi Wei, Xinghua Zhu, Jing Xiao
This paper investigates a novel task of talking face video generation solely from speeches.
no code implementations • 26 Feb 2021 • Jie Zhao, Xinghua Zhu, Jianzong Wang, Jing Xiao
In this paper an efficient method is proposed to evaluate the contributions of federated participants.
no code implementations • 24 Feb 2021 • Yong liu, Xinghua Zhu, Jianzong Wang, Jing Xiao
In addition, using the proposed metric, we investigate the influential factors of risk level.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Xinghua Zhu, Jianzong Wang, Zhenhou Hong, Jing Xiao
It is also found that the FL models are sensitive to data load balancedness among client datasets.
5 code implementations • 27 Jul 2020 • Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr
Federated learning (FL) is a rapidly growing research field in machine learning.