3 papers
cs.CR2026
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
Xinpeng Ling, Jie Fu, Kuncan Wang +3
Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persiste…
cs.LG2025
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Tong Cheng, Jie Fu, Xinpeng Ling +4
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from var…
cs.LG2025
scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
Huifa Li, Jie Fu, Xinlin Zhuang +6
Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due…