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Found 4 papers, 0 papers with code
On provable privacy vulnerabilities of graph representations
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations.
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs.
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy
FedWalk is designed to offer centralized competitive graph representation capability with data privacy protection and great communication efficiency.
