1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
Wentao Yu, Bo Han, Jie Yang +1
Graph Federated Learning (GFL) enables collaborative representation learning across distributed subgraphs while preserving privacy. However, heterogeneity remains a critical challe…
cs.LG2026
Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
Wentao Yu, Sheng Wan, Shuo Chen +2
Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from di…
cs.LG2024★ 1 cited
Robust Learning under Hybrid Noise
Yang Wei, Shuo Chen, Shanshan Ye +2
Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal…