3 papers
cs.LG2026
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
Zhishuai Guo, Wenhan Wu, Chen Chen +3
Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to priva…
cs.AI2025
REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature
Wenhan Wu, Zhili He, Huanghuang Liang +4
Data-protection regulations such as the GDPR grant every participant in a federated system a right to be forgotten. Federated unlearning has therefore emerged as a research frontie…
cs.LG2025
Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization
Wenhan Wu, Zheyuan Liu, Chongyang Gao +2
Current LLM unlearning methods face a critical security vulnerability that undermines their fundamental purpose: while they appear to successfully remove sensitive or harmful knowl…