37 citations · 69 across the 20 of their papers we have counts for
4 papers · 1 filter
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Weizhao Jin, Yuhang Yao, Shanshan Han +5
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated…
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng +3
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera…
Faithful Explanations for Deep Graph Models
Zifan Wang, Yuhang Yao, Chaoran Zhang +5
This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations f…
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
Yuhang Yao, Carlee Joe-Wong
We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time, e.g., due to community migrati…