Equipping Federated Graph Neural Networks with Structure-aware Group Fairness

Nan Cui, Xiuling Wang, Wendy Hui Wang, Violet Chen, Yue Ning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Graph Neural Networks (GNNs) are used for graph data processing across various domains. Centralized training of GNNs often faces challenges due to privacy and regulatory issues, making federated learning (FL) a preferred solution in a distributed paradigm. However, GNNs may inherit biases from training data, causing these biases to propagate to the global model in distributed scenarios. To address this issue, we introduce F2GNN, a Fair Federated Graph Neural Network, to enhance group fairness. Recognizing that bias originates from both data and algorithms, F2GNN aims to mitigate both types of bias under federated settings. We offer theoretical insights into the relationship between data bias and statistical fairness metrics in GNNs. Building on our theoretical analysis, F2GNN features a fairness-aware local model update scheme and a fairness-weighted global model update scheme, considering both data bias and local model fairness during aggregation. Empirical evaluations show F2GNN outperforms SOTA baselines in fairness and accuracy.

Original languageEnglish
Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
EditorsGuihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu
Pages980-985
Number of pages6
ISBN (Electronic)9798350307887
DOIs
StatePublished - 2023
Event23rd IEEE International Conference on Data Mining, ICDM 2023 - Shanghai, China
Duration: 1 Dec 20234 Dec 2023

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference23rd IEEE International Conference on Data Mining, ICDM 2023
Country/TerritoryChina
CityShanghai
Period1/12/234/12/23

Keywords

  • Federated Learning
  • Graph Neural Networks
  • Group Fairness

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