Wenke Huang

Orcid: 0000-0003-4819-293X

According to our database1, Wenke Huang authored at least 13 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2024
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning.
IEEE Trans. Pattern Anal. Mach. Intell., February, 2024

Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding.
IEEE Trans. Image Process., 2024

Federated Learning With Long-Tailed Data via Representation Unification and Classifier Rectification.
IEEE Trans. Inf. Forensics Secur., 2024

Robust Attitude Tracking Control Based on Adaptive Dynamic Programming for Flexible Dumbbell-Shaped Spacecraft.
IEEE Trans. Aerosp. Electron. Syst., 2024

Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity.
CoRR, 2024

Federated Graph Learning under Domain Shift with Generalizable Prototypes.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.
CoRR, 2023

Dynamic Personalized Federated Learning with Adaptive Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Federated Graph Semantic and Structural Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Rethinking Federated Learning with Domain Shift: A Prototype View.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Few-Shot Model Agnostic Federated Learning.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Towards Hardware-Friendly and Robust Facial Landmark Detection Method.
Proceedings of the Neural Information Processing - 29th International Conference, 2022

Learn from Others and Be Yourself in Heterogeneous Federated Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022


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