Chenhan Zhang

Orcid: 0000-0002-2352-0485

According to our database¹, Chenhan Zhang authored at least 35 papers between 2019 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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On csauthors.net:

Bibliography

2024

An Asynchronous Multi-Task Semantic Communication Method.

[BibT_eX]

[DOI]

IEEE Netw., July, 2024

Generative Adversarial Networks: A Survey on Attack and Defense Perspective.

[BibT_eX]

[DOI]

ACM Comput. Surv., April, 2024

Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling.

[BibT_eX]

[DOI]

Chenhan Zhang

Zhenlei Wang

Comput. Chem. Eng., February, 2024

Fast Fourier Inception Networks for Occluded Video Prediction.

[BibT_eX]

[DOI]

Ping Li

Chenhan Zhang

Xianghua Xu

IEEE Trans. Multim., 2024

Forgetting and Remembering Are Both You Need: Balanced Graph Structure Unlearning.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2024

Machine Unlearning via Representation Forgetting With Parameter Self-Sharing.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2024

The Role of Class Information in Model Inversion Attacks Against Image Deep Learning Classifiers.

[BibT_eX]

[DOI]

IEEE Trans. Dependable Secur. Comput., 2024

2023

SAM: Query-efficient Adversarial Attacks against Graph Neural Networks.

[BibT_eX]

[DOI]

ACM Trans. Priv. Secur., November, 2023

Optimal scheduling of ethylene plants under uncertainty: An unsupervised learning-based data-driven strategy.

[BibT_eX]

[DOI]

Chenhan Zhang

Zhenlei Wang

Comput. Ind. Eng., September, 2023

Toward Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach.

[BibT_eX]

[DOI]

IEEE Internet Things J., March, 2023

Data-Driven Shape Sensing in Continuum Manipulators via Sliding Resistive Flex Sensors.

[BibT_eX]

[DOI]

CoRR, 2023

Pair-wise Layer Attention with Spatial Masking for Video Prediction.

[BibT_eX]

[DOI]

CoRR, 2023

RUE: Realising Unlearning from the Perspective of Economics.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on Trust, 2023

Inferring Private Data from AI Models in Metaverse through Black-box Model Inversion Attacks.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Metaverse Computing, 2023

Construct New Graphs Using Information Bottleneck Against Property Inference Attacks.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Communications, 2023

FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Communications, 2023

CP-FL: Practical Gradient Leakage Defense in Federated Learning with Compressive Privacy.

[BibT_eX]

[DOI]

Proceedings of the IEEE Global Communications Conference, 2023

Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, 2023

BFU: Bayesian Federated Unlearning with Parameter Self-Sharing.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, 2023

2022

Long-Term Origin-Destination Demand Prediction With Graph Deep Learning.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2022

Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach.

[BibT_eX]

[DOI]

IEEE Internet Things J., 2022

A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting.

[BibT_eX]

[DOI]

IEEE Internet Things J., 2022

Challenges and future directions of secure federated learning: a survey.

[BibT_eX]

[DOI]

Frontiers Comput. Sci., 2022

Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE Wireless Communications and Networking Conference, 2022

GSMI: A Gradient Sign Optimization Based Model Inversion Method.

[BibT_eX]

[DOI]

Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

2021

FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting.

[BibT_eX]

[DOI]

IEEE Trans. Ind. Informatics, 2021

Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Soc. Syst., 2021

Origin-Destination Matrix Prediction via Hexagon-based Generated Graph.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

Learn Travel Time Distribution with Graph Deep Learning and Generative Adversarial Network.

[BibT_eX]

[DOI]

Xiaozhuang Song

Chenhan Zhang

James J. Q. Yu

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

TSTNet: A Sequence to Sequence Transformer Network for Spatial-Temporal Traffic Prediction.

[BibT_eX]

[DOI]

Xiaozhuang Song

Ying Wu

Chenhan Zhang

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Safeguard the Original Data in Federated Learning via Data Decomposition.

[BibT_eX]

[DOI]

Jing Lin

Chenhan Zhang

Shui Yu

Proceedings of the IEEE Global Communications Conference, 2021

2020

Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel.

[BibT_eX]

[DOI]

Chenhan Zhang

CoRR, 2020

FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting.

[BibT_eX]

[DOI]

Proceedings of the IEEE Global Communications Conference, 2020

2019

Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting.

[BibT_eX]

[DOI]

Chenhan Zhang

James Jian Qiao Yu

Yi Liu

IEEE Access, 2019

Chenhan Zhang

Timeline

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