Wenke Huang

Orcid: 0000-0003-4819-293X

Affiliations:
  • Wuhan University, China


According to our database1, Wenke Huang authored at least 43 papers between 2022 and 2025.

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

Timeline

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Online presence:

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Bibliography

2025
Lightweight Federated Domain Generalization With Global-Local Contrastive Learning for Machine Fault Diagnosis.
IEEE Internet Things J., October, 2025

MAPO: Mixed Advantage Policy Optimization.
CoRR, September, 2025

Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency.
CoRR, August, 2025

Positive Style Accumulation: A Style Screening and Continuous Utilization Framework for Federated DG-ReID.
CoRR, July, 2025

S2FGL: Spatial Spectral Federated Graph Learning.
CoRR, July, 2025

An Empirical Study of Federated Prompt Learning for Vision Language Model.
CoRR, May, 2025

ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation.
CoRR, May, 2025

Backdoor Cleaning without External Guidance in MLLM Fine-tuning.
CoRR, May, 2025

CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process.
CoRR, May, 2025

Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks.
CoRR, April, 2025

3D Human Interaction Generation: A Survey.
CoRR, March, 2025

Privacy-Enhancing Paradigms within Federated Multi-Agent Systems.
CoRR, March, 2025

Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model.
CoRR, March, 2025

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations.
CoRR, February, 2025

Gradient and Structure Consistency in Multimodal Emotion Recognition.
IEEE Trans. Image Process., 2025

Kindle Federated Generalization With Domain Specialized and Invariant Knowledge.
IEEE Trans. Inf. Forensics Secur., 2025

Self-knowledge distillation with dimensional history knowledge.
Sci. China Inf. Sci., 2025

An Empirical Study of Federated Prompt Learning for Vision Language Model.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Pixel-wise Divide and Conquer for Federated Vessel Segmentation.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Energy-based Backdoor Defense Against Federated Graph Learning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

EMOE: Modality-Specific Enhanced Dynamic Emotion Experts.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

Label-Free Backdoor Attacks in Vertical Federated Learning.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.
IEEE Trans. Pattern Anal. Mach. Intell., December, 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

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning.
CoRR, 2024

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning.
Proceedings of the Computer Vision - ECCV 2024, 2024

FedAS: Bridging Inconsistency in Personalized Federated Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Fair Federated Learning Under Domain Skew with Local Consistency and Domain Diversity.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

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

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

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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