2 citations · 3 across the 19 of their papers we have counts for
6 papers · 1 filter
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
S2FGL: Spatial Spectral Federated Graph Learning
Zihan Tan, Suyuan Huang, Guancheng Wan +3
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Cu…
An Empirical Study of Federated Prompt Learning for Vision Language Model
Zhihao Wang, Wenke Huang, Tian Chen +7
The Vision Language Model (VLM) excels in aligning vision and language representations, and prompt learning has emerged as a key technique for adapting such models to downstream ta…
ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation
Jian Liang, Wenke Huang, Xianda Guo +3
Low-Rank Adaptation (LoRA) is widely adopted for downstream fine-tuning of foundation models due to its efficiency and zero additional inference cost. Many real-world applications…
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
Yuang Jia, Xiaojuan Shan, Jun Xia +5
Data-free Knowledge Distillation (DFKD) is a method that constructs pseudo-samples using a generator without real data, and transfers knowledge from a teacher model to a student by…
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…