Wei Shen

I'm a master student at Wuhan University. Before that, I obtained my B.S. degree from Wuhan University in 2023.

My research interests lie in Trustworthy Machine Learning, with a focus on Large Language Models, Federated Learning, and Graph Learning. I am committed to developing practical, generalizable algorithms that enhance the robustness and explainability of ML systems, making them controllable.

Email / Google Scholar / Github

News

[09/2025] Our benchmark about realistic evaluations in VFL has been accepted by NeurIPS 2025 as spotlight presentation. See you in San Diego!
[02/2025] Our paper about adressing limited aligned samples in VFL has been accepted by IEEE Transactions on Mobile Computing (TMC).
[12/2024] Our paper about backdoor attacks in VFL has been accepted at AAAI 2025. See you in Philadelphia!
[07/2024] Our paper about handling oversmoothing in GNN has been accepted at ACM MM 2024. See you in Melbourne!

Selected Publications

	DecepChain: Inducing Deceptive Reasoning in Large Language Models [paper] [project] Wei Shen⁺, Han Wang⁺, Haoyu Li⁺, Huan Zhang arxiv, 2025 We present that the attackers could induce LLMs to generate incorrect yet coherent CoTs that look plausible at first glance.
	MARS-VFL: A Unified Benchmark for Vertical Federated Learning with Realistic Evaluation (Spotlight) [paper] [codes] Wei Shen, Weiqi Liu, Mingde Chen, Wenke Huang, Mang Ye The 39th Conference on Neural Information Processing Systems (NeurIPS), 2025 We propose a unified benchmark for realistic VFL evaluation that integrates data from practical applications.
	Label-Free Backdoor Attacks in Vertical Federated Learning [paper] [codes] Wei Shen⁺, Wenke Huang⁺, Guancheng Wan, Mang Ye The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2025 We propose a backdoor paradigm for VFL that operates without explicit label information.
	Build Yourself Before Collaboration: Vertical Federated Learning with Limited Aligned Samples [paper] [codes] Wei Shen, Mang Ye, Wei Yu, Pong C. Yuen IEEE Transactions on Mobile Computing (TMC), 2025 We address the challenge of limited aligned samples in VFL by implementing local learning before each collaboration.
	Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling [paper] [codes] Wei Shen, Mang Ye, Wenke Huang ACM Multimedia (ACM MM), 2024 We address the oversmoothing issue in GNNs by leveraging both instance-level and dimension-level cues.

Awards

National Scholarship, 2025.11.
NeurIPS Financial Aid Award, 2025.10
Tencent Scholarship (Special Prize), 2025.10
DiDi Scholarship, 2025.10
National Encouragement Scholarship, 2022.09

Service

Conference Reviewer: ICLR 2025/2026, CVPR 2024/2025/2026, AAAI 2026.

Thanks for the template from Jon Barron.