Jiaru Qian — accepted AI-conference papers

2025

GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention

Jiaru Qian, G. W., Wenke Huang, Guibin Zhang, Yuxin Wu, Bo Du +1

ICML 2025poster

Federated Graph Learning (FGL) proposes an effective approach to collaboratively training Graph Neural Networks (GNNs) while maintaining privacy. Nevertheless, communication efficiency becomes a critical bottleneck in environments with limited resources. In this context, one-shot FGL emerges as a pr…

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2025

OASIS: One-Shot Federated Graph Learning via Wasserstein Assisted Knowledge Integration

Guancheng Wan, Jiaru Qian, Wenke Huang, Qilin Xu, Xianda Guo, Boheng Li +3

NeurIPS 2025poster

Federated Graph Learning (FGL) offers a promising framework for collaboratively training Graph Neural Networks (GNNs) while preserving data privacy. In resource-constrained environments, One-shot Federated Learning (OFL) emerges as an effective solution by limiting communication to a single round. C…

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