51 citations · 52 across the 4 of their papers we have counts for
5 papers
FedPURIN: Programmed Update and Reduced INformation for Sparse Personalized Federated Learning
Lunchen Xie, Zehua He, Qingjiang Shi
Personalized Federated Learning (PFL) has emerged as a critical research frontier addressing data heterogeneity issue across distributed clients. Novel model architectures and coll…
ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search Algorithm
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Differentiable Neural Architecture Search (NAS) provides a promising avenue for automating the complex design of deep learning (DL) models. However, current differentiable NAS meth…
A Lightweight Neural Architecture Search Model for Medical Image Classification
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and…
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
Lunchen Xie, Jiaqi Liu, Songtao Lu +2
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big d…
Efficient Algorithms for Rotation Averaging Problems
Yihong Dong, Lunchen Xie, Qingjiang Shi
The rotation averaging problem is a fundamental task in computer vision applications. It is generally very difficult to solve due to the nonconvex rotation constraints. While a suf…