Federated Learning

Jan 27, 2020

Machine Learning Federated Learning Distributed Learning

Junyuan "Jason" Hong

Incoming Assistant Professor

My research interest lies in the interaction of responsible AI and healthcare.

Publications

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

Tracking IP leakage in federated learning.

Shuyang Yu, Junyuan "Jason" 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

We develop a hybrid federated learning for learning financial-crime predictive models from horizontal and vertical federated data structures.

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

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

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 "Jason" 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 "Jason" 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 "Jason" 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 "Jason" 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 "Jason" 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 Transferable Representations

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

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

Federated Adversarial Debiasing for Fair and Transferable 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 "Jason" Hong, Jiayu Zhou

Data-Free Knowledge Distillation for Heterogeneous Federated Learning