A federated graph neural network framework for privacy-preserving personalization

By Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie. Nature Communications, 2021

328 citations recorded by the source corpus.

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization. Mainstream personalization methods rely on centralized Graph Neural Network learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, the authors present a federated GNN framework for both effective and privacy-preserving personalization.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/5bf57b615bfce122792295fd7a4e89d230787ccc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lichao Sun
2. Lingjuan Lyu
3. Philip S. Yu
4. Qiang Yang
5. Han Yu
6. Jun Zhao
7. Xingjun Ma
8. Carl Yang

In Information Systems

1. Lingjuan Lyu
2. Philip S. Yu
3. Carl Yang
4. Jun Zhao
5. Fei Sun
6. Bin Cui
7. Qiang Yang
8. Han Yu

Citations in the corpus

• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “However, the local user data only contains first-order user-item interactions, which is challenging to capture high-order connectivity without privacy linkage [166].”
• Subgraph Federated Learning with Missing Neighbor Generation (2021, methodology): “There are existing FL frameworks designed for the graph data learning task (He et al., 2021a; Wu et al., 2021).”
• Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “In recent years, FL has benefited a wide range of applications such as next word prediction [6], [7], visual object detection for safety [8], entity resolution [9], recommendation [10], [11], [12], industrial IoT [13], and graph-based analysis [14], [15], [16], etc.”