8 citations · 11 across the 16 of their papers we have counts for
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cs.CL2024★ 1 cited
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
Wenke Huang, Jian Liang, Zekun Shi +6
Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets.…
cs.LG2024★ 2 cited
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Zihan Tan, Guancheng Wan, Wenke Huang +1
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized re…
cs.LG2024
Federated Graph Semantic and Structural Learning
Wenke Huang, Guancheng Wan, Mang Ye +1
Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the…