Shuyuan Wu
Biographic Data
| ID | 8920733 |
|---|---|
| NAME | Shuyuan Wu |
| GIVEN NAMES | Shuyuan |
| FAMILY NAME | Wu |
| SIGNATURE | WU S |
| AFFILIATIONS | Peking University |
| ORCID | 0009-0000-4203-5103 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Network Gradient Descent Algorithm for Decentralized Federated Learning
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed networ…
Feature Screening for Massive Data Analysis by Subsampling
Modern statistical analysis often encounters massive datasets with ultrahigh-dimensional features. In this work, we develop a subsampling approach for feature screening with massive datasets. The approach is implemented by repeated subsampling of massive data and can be used for analyzing tasks with memory constraints. To conduct the procedure, we first calculate an R-squared screening measure (and related sample moments) based on subsamples. Sec…
No prominent works on this page.
Feature Screening for Massive Data Analysis by Subsampling
Modern statistical analysis often encounters massive datasets with ultrahigh-dimensional features. In this work, we develop a subsampling approach for feature screening with massive datasets. The approach is implemented by repeated subsampling of massive data and can be used for analyzing tasks with memory constraints. To conduct the procedure, we first calculate an R-squared screening measure (and related sample moments) based on subsamples. Sec…
Network Gradient Descent Algorithm for Decentralized Federated Learning
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed networ…
Artificial Intelligence (2 works) · Computer Science (2 works) · Advanced Statistical Methods and Models (1 work) · Algorithm (1 work) · Artificial neural network (1 work) · Bayesian Methods and Mixture Models (1 work) · Data mining (1 work) · Engineering (1 work) · Gradient descent (1 work) · Jackknife resampling (1 work)