ArticleNature communications2022
A federated graph neural network framework for privacy-preserving personalization.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.Journal of pharmaceutical analysis · 2026Review
- Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture.Scientific reports · 2026Article
- Evolutionary Digital Twin for Oil and Gas Pipelines: A Cognitive Multi-Agent Framework with Continuous Feedback Learning.Sensors (Basel, Switzerland) · 2026Article
- VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs.Interdisciplinary sciences, computational life sciences · 2026Article
- Confidence-calibrated federated graph attention for internet of things agents under latency SLOs.Scientific reports · 2026Article
- Privacy-preserving federated prediction of health outcomes using multi-center survey data.BMC medical research methodology · 2026Article
- FedGraphHE: A privacy-preserving federated graph neural network framework with dynamic homomorphic encryption and robust aggregation.PloS one · 2026Article
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- Incentivizing inclusive contributions in model sharing markets.Nature communications · 2025Article
- Geometric Deep Learning for Multimodal Data in CKD.Journal of the American Society of Nephrology : JASN · 2025Article
- Federated learning for network attack detection using attention-based graph neural networks.Scientific reports · 2024Article
- Hardware-accelerated integrated optoelectronic platform towards real-time high-resolution hyperspectral video understanding.Nature communications · 2024Article
- Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.JMIR aging · 2024Article
- Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.Nature communications · 2023Article
- Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey.SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining · 2023Article
- A federated graph neural network framework for privacy-preserving personalization.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.