22 citations · 48 across the 7 of their papers we have counts for
4 papers · 1 filter
Practical Differentially Private and Byzantine-resilient Federated Learning
Zihang Xiang, Tianhao Wang, Wanyu Lin +1
Privacy and Byzantine resilience are two indispensable requirements for a federated learning (FL) system. Although there have been extensive studies on privacy and Byzantine securi…
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
Wanyu Lin, Hao Lan, Hao Wang +1
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Spe…
Towards Private Learning on Decentralized Graphs with Local Differential Privacy
Wanyu Lin, Baochun Li, Cong Wang
Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profil…
Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, Baochun Li
This paper presents Gem, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of…