51 citations · 56 across the 5 of their papers we have counts for
6 papers
Prototype-guided Bilateral Alignment Multimodal Federated Learning
Tianchi Liao Tianchi_Liao, Lele Fu, Sheng Huang +3
Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing methods predominantly rely on…
Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning
Shutong Zheng, Lele Fu, Sheng Huang +2
One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volume-based weighting captures th…
THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings
Bowen Deng, Tong Wang, Lele Fu +3
Graph node clustering is a fundamental unsupervised task. Existing methods typically train an encoder through selfsupervised learning and then apply K-means to the encoder output.…
Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations
Chuan Chen, Tianchi Liao, Xiaojun Deng +3
In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distribution…
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
Yuecheng Li, Lele Fu, Sheng Huang +3
Recently, graph contrastive learning (GCL) has emerged as one of the optimal solutions for node-level and supervised tasks. However, for structure-related and unsupervised tasks su…
Subspace-Contrastive Multi-View Clustering
Fu Lele, Zhang Lei, Yang Jinghua +3
Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information…