54 citations · 168 across the 17 of their papers we have counts for
6 papers · 1 filter
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…
Differentiable Search of Accurate and Robust Architectures
Yuwei Ou, Xiangning Xie, Shangce Gao +3
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has…
Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye +1
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global m…
Partial Differential Equations Meet Deep Neural Networks: A Survey
Shudong Huang, Wentao Feng, Chenwei Tang +1
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following P…
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Pengxin Zeng, Yunfan Li, Peng Hu +3
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e.g.}, gender, race, RNA sequencing technique) from dominating the cluster…
XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunnan Li, Ivor W. Tsang +3
In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, r…