7 citations · 30 across the 27 of their papers we have counts for
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cs.LG2023★ 2 cited
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
Rui Ye, Yaxin Du, Zhenyang Ni +2
In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data hetero…
cs.LG2023★ 1 cited
Federated Learning Empowered by Generative Content
Rui Ye, Xinyu Zhu, Jingyi Chai +2
Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way. However, data heterogeneity significantly limits the performance…
cs.LG2023
Hypergraph Transformer for Semi-Supervised Classification
Zexi Liu, Bohan Tang, Ziyuan Ye +3
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for…