68 citations · 78 across the 7 of their papers we have counts for
7 papers
InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization
Xueyang Wu, Siyuan Liu, Kezhuo Yang +1
Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic informatio…
Delving into Differentially Private Transformer
Youlong Ding, Xueyang Wu, Yining Meng +3
Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accurac…
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
Youlong Ding, Xueyang Wu, Hao Wang +1
The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer mode…
Revisiting Hyperparameter Tuning with Differential Privacy
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…
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Youlong Ding, Xueyang Wu, Zhitao Li +5
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network…
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
Xueyang Wu, Shengqi Tan, Qian Xu +1
Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there h…
Real-World Image Datasets for Federated Learning
Jiahuan Luo, Xueyang Wu, Yun Luo +4
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. While…