66 citations · 71 across the 4 of their papers we have counts for
4 papers
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
Junyuan Hong, Lingjuan Lyu, Jiayu Zhou +1
As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training…
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
Haotao Wang, Junyuan Hong, Aston Zhang +2
Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous works have shown it extremely challenging to unlearn the undesired backdoor behavior from the network, sinc…
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Junyuan Hong, Haotao Wang, Zhangyang Wang +1
Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, partic…
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data…