FedFairGNN: A Privacy-Preserving and Fairness-Aware Federated Graph Neural Network for Fraud Detection

Quang-Vinh Dang, Ngoc-Son-An Nguyen
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:74-86, 2026.

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for fraud detection. However, existing approaches often rely on centralised training, which raises privacy concerns when data is distributed across institutions. Federated Learning (FL) addresses this but faces unique challenges on graph data: graph heterogeneity across clients and propagation of algorithmic bias. We propose FedFairGNN, a novel framework that simultaneously ensures privacy, fairness, and utility via three components: Fairness-Sensitive Edge Reweighting (FSER), Fairness-Task Gradient Decomposition (FTGD) with Differential Privacy, and Bi-Objective Frank-Wolfe Aggregation (BFWA). Experiments on YelpChi, Amazon, and Elliptic datasets with $K = 3$ clients demonstrate that FedFairGNN achieves a highly competitive performance-fairness trade-off while significantly reducing demographic disparity.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-dang26a, title = {{FedFairGNN}: A Privacy-Preserving and Fairness-Aware Federated Graph Neural Network for Fraud Detection}, author = {Dang, Quang-Vinh and Nguyen, Ngoc-Son-An}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {74--86}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/dang26a/dang26a.pdf}, url = {https://proceedings.mlr.press/v319/dang26a.html}, abstract = {Graph Neural Networks (GNNs) have emerged as a powerful tool for fraud detection. However, existing approaches often rely on centralised training, which raises privacy concerns when data is distributed across institutions. Federated Learning (FL) addresses this but faces unique challenges on graph data: graph heterogeneity across clients and propagation of algorithmic bias. We propose FedFairGNN, a novel framework that simultaneously ensures privacy, fairness, and utility via three components: Fairness-Sensitive Edge Reweighting (FSER), Fairness-Task Gradient Decomposition (FTGD) with Differential Privacy, and Bi-Objective Frank-Wolfe Aggregation (BFWA). Experiments on YelpChi, Amazon, and Elliptic datasets with $K = 3$ clients demonstrate that FedFairGNN achieves a highly competitive performance-fairness trade-off while significantly reducing demographic disparity.} }
Endnote
%0 Conference Paper %T FedFairGNN: A Privacy-Preserving and Fairness-Aware Federated Graph Neural Network for Fraud Detection %A Quang-Vinh Dang %A Ngoc-Son-An Nguyen %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-dang26a %I PMLR %P 74--86 %U https://proceedings.mlr.press/v319/dang26a.html %V 319 %X Graph Neural Networks (GNNs) have emerged as a powerful tool for fraud detection. However, existing approaches often rely on centralised training, which raises privacy concerns when data is distributed across institutions. Federated Learning (FL) addresses this but faces unique challenges on graph data: graph heterogeneity across clients and propagation of algorithmic bias. We propose FedFairGNN, a novel framework that simultaneously ensures privacy, fairness, and utility via three components: Fairness-Sensitive Edge Reweighting (FSER), Fairness-Task Gradient Decomposition (FTGD) with Differential Privacy, and Bi-Objective Frank-Wolfe Aggregation (BFWA). Experiments on YelpChi, Amazon, and Elliptic datasets with $K = 3$ clients demonstrate that FedFairGNN achieves a highly competitive performance-fairness trade-off while significantly reducing demographic disparity.
APA
Dang, Q. & Nguyen, N.. (2026). FedFairGNN: A Privacy-Preserving and Fairness-Aware Federated Graph Neural Network for Fraud Detection. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:74-86 Available from https://proceedings.mlr.press/v319/dang26a.html.

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