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cs.LG2024
FedRGL: Robust Federated Graph Learning for Label Noise
De Li, Haodong Qian, Qiyu Li +4
Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clien…
cs.LG2024
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective
De Li, Xianxian Li, Zeming Gan +3
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when nois…
cs.LG2020★ 1 cited
SSGD: A safe and efficient method of gradient descent
Jinhuan Duan, Xianxian Li, Shiqi Gao +2
With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent metho…