5 citations · 9 across the 4 of their papers we have counts for
4 papers
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
Chulin Xie, De-An Huang, Wenda Chu +4
Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high…
Distributed Robust Principal Component Analysis
Wenda Chu
We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when t…
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang +5
Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to…
TPC: Transformation-Specific Smoothing for Point Cloud Models
Wenda Chu, Linyi Li, Bo Li
Point cloud models with neural network architectures have achieved great success and have been widely used in safety-critical applications, such as Lidar-based recognition systems…