Federated Learning

Jan 27, 2020

Machine Learning Federated Learning Distributed Learning

Junyuan Hong

Postdoctoral Fellow

My research interests include data privacy and trustworthy machine learning.

Publications

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning

Tracking IP leakage in federated learning.

Shuyang Yu, Junyuan Hong, Yi Zeng, Fei Wang, Ruoxi Jia, Jiayu Zhou

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of …

Haobo Zhang, Junyuan Hong, Fan Dong, Steve Drew, Liangjie Xue, Jiayu Zhou

FedNoisy: A Federated Noisy Label Learning Benchmark

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of …

Siqi Liang, Jintao Huang, Junyuan Hong, Fan Dong, Dun Zeng, Jiayu Zhou, Zenglin Xu

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from …

Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou

Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning

Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without …

Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou

Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems

The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. …

Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

Efficient and federated learning for heterogeneous clients with different memory sizes

Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

Federated Adversarial Debiasing for Fair and Trasnferable Representations

A distributed domain/group debiasing framework for unsupervised domain adaptation or fairness enhancement.

Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Hiroko Dodge, Zhangyang Wang, Jiayu Zhou

Federated Adversarial Debiasing for Fair and Trasnferable Representations

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model …

Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou

Data-Free Knowledge Distillation for Heterogeneous Federated Learning