13 citations · 23 across the 8 of their papers we have counts for
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cs.LG2024
One Node Per User: Node-Level Federated Learning for Graph Neural Networks
Zhidong Gao, Yuanxiong Guo, Yanmin Gong
Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a sol…
cs.LG2024
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
Zhidong Gao, Yu Zhang, Yanmin Gong +1
Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy…
cs.LG2020
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing
Zhidong Gao, Rui Hu, Yanmin Gong
Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbatio…