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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
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…