411 citations · 825 across the 13 of their papers we have counts for
7 papers · 1 filter
Decentralized Federated Learning: A Survey and Perspective
Liangqi Yuan, Ziran Wang, Lichao Sun +2
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing…
Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation
Yixin Liu, Haohui Ye, Kai Zhang +1
The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researc…
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
Jifeng Hu, Yanchao Sun, Hechang Chen +4
Multi-agent reinforcement learning has drawn increasing attention in practice, e.g., robotics and automatic driving, as it can explore optimal policies using samples generated by i…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…
Differentially Private Deep Learning with Smooth Sensitivity
Lichao Sun, Yingbo Zhou, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is th…
Private Model Compression via Knowledge Distillation
Ji Wang, Weidong Bao, Lichao Sun +3
The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs noto…