no code implementations • 28 May 2023 • Youlong Ding, Xueyang Wu, Hao Wang, Weike Pan
The Transformer has emerged as a versatile and effective architecture with broad applications.
no code implementations • 3 Nov 2022 • Youlong Ding, Xueyang Wu
Hyperparameter tuning is a common practice in the application of machine learning but is a typically ignored aspect in the literature on privacy-preserving machine learning due to its negative effect on the overall privacy parameter.
no code implementations • 21 Jun 2022 • Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan, Qiang Yang
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
no code implementations • 21 Jun 2022 • Xueyang Wu, Shengqi Tan, Qian Xu, Qiang Yang
The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.
no code implementations • 29 Sep 2021 • Xueyang Wu, Hengguan Huang, Hao Wang, Ye Wang, Qian Xu
However, it is challenging for GANs to model distributions of separate non-i. i. d.
2 code implementations • 14 Oct 2019 • Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yun-Feng Huang, Yang Liu, Qiang Yang
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.