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  3. Utility-preserving Federated Graph Learning With Dual-perspective Fairness.
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  3. Utility-preserving Federated Graph Learning With Dual-perspective Fairness.

Related Experiment Videos

Utility-Preserving Federated Graph Learning with Dual-Perspective Fairness.

Renqiang Luo, Huafei Huang, Shuo Yu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 30, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces F3GL, a novel federated graph learning method that improves fairness for both servers and clients without reducing performance. F3GL leverages spectral graph theory for enhanced dual-perspective fairness in federated graph neural networks (FedGNNs).

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Neural Networks

    Background:

    • Federated graph neural networks (FedGNNs) face challenges in achieving fairness from both server (global) and client (local) perspectives simultaneously.
    • Existing fairness-aware methods often sacrifice model utility (performance) to achieve fairness in distributed learning settings.
    • The utility sacrifice is particularly pronounced in federated frameworks, exacerbating fairness challenges.

    Purpose of the Study:

    • To propose F3GL, a dual-perspective fairness federated graph learning method.
    • To enhance both global and local fairness in FedGNNs while preserving model utility.
    • To provide theoretical insights into fairness preservation using spectral graph theory.

    Main Methods:

    • Developed F3GL, a novel dual-perspective fairness federated graph learning approach.
  • Conducted theoretical analysis using spectral graph theory to understand feature similarity after convolution.
  • Identified the principal eigenvalue's role in enhancing feature similarity and applied a specialized eigenvalue selection strategy.

    • Main Results:

    • Demonstrated that the principal eigenvalue is key to enhancing similarity between original and convolved sensitive features.
    • Showcased that the theoretical findings are universally applicable to both clients and servers in federated learning.
    • Experimental results on real-world datasets confirm F3GL's superiority over existing methods in improving dual-perspective fairness without utility loss.

    Conclusions:

    • F3GL effectively enhances dual-perspective fairness (global and local) in federated graph learning.
    • The proposed method preserves utility, overcoming a key limitation of prior fairness-aware approaches.
    • Spectral graph theory provides a robust theoretical foundation for achieving fairness in FedGNNs.