4 papers
Faster Learning under Relaxed Local Differential Privacy
Cristina Butucea, Huiyun Tang, Marie-Luce Taupin
We consider density estimation under the relaxed local differential privacy condition that the privatized distributions are -close in total variation distance. We show that addi…
Enterprise Profit Prediction Using Multiple Data Sources with Missing Values through Vertical Federated Learning
Huiyun Tang, Feifei Wang, Long Feng +1
Small and medium-sized enterprises (SMEs) play a crucial role in driving economic growth. Monitoring their financial performance and discovering relevant covariates are essential f…
Data Privatization in Vertical Federated Learning with Client-wise Missing Problem
Huiyun Tang, Long Feng, Yang Li +1
Vertical Federated Learning (VFL) often suffers from client-wise missingness, where entire feature blocks from some clients are unobserved, and conventional approaches are vulnerab…
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
Feifei Wang, Huiyun Tang, Yang Li
Federated learning is an emerging distributed machine learning framework aiming at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning,…