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cs.LG2024
Training Fair Models in Federated Learning without Data Privacy Infringement
Xin Che, Jingdi Hu, Zirui Zhou +2
Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data…
cs.LG2024
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
Qiying Pan, Yifei Zhu, Lingyang Chu
Graph neural networks (GNN) have been widely deployed in real-world networked applications and systems due to their capability to handle graph-structured data. However, the growing…