27 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.DC2024
Federated Graph Learning with Adaptive Importance-based Sampling
Anran Li, Yuanyuan Chen, Chao Ren +5
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is sca…
cs.LG2024★ 5 cited
Advances and Open Challenges in Federated Foundation Models
Chao Ren, Han Yu, Hongyi Peng +9
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabil…
cs.DC2022★ 27 cited
Federated Graph Neural Networks: Overview, Techniques and Challenges
Rui Liu, Pengwei Xing, Zichao Deng +3
With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent y…