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GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Extensive experimentation on real-world datasets showcases the efficacy of GRAPHGINI in making significant improvements in individual fairness compared to all currently available state-of-the-art methods while maintaining utility and group equality.
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.)
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