Papers › A federated graph neural network framework for privacy-preserving personalization

A federated graph neural network framework for privacy-preserving personalization

2 Jun 2022Nature Communications 2022 6archive 2025-07-28

Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi, Yongfeng Huang, Xing Xie

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.

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wuch15/fedpergnn mentioned in papertf report

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Tasks

Federated Learning Graph Neural Network Privacy Preserving Recommendation Systems

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Recommendation Systems	Douban	FedPerGNN	RMSE	0.775	#4 of 7	Archive leaderboard	report
Recommendation Systems	Flixster	FedPerGNN	RMSE	0.980	#2 of 4	Archive leaderboard	report
Recommendation Systems	MovieLens 100K	FedPerGNN	RMSE	0.910	#17 of 18	Archive leaderboard	report
Recommendation Systems	MovieLens 10M	FedPerGNN	RMSE	0.793	#11 of 17	Archive leaderboard	report
Recommendation Systems	MovieLens 1M	FedPerGNN	RMSE	0.839	#9 of 31	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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