  Ensuring fairness in Recommendation Systems (RSs) across demographic groups
is critical due to the increased integration of RSs in applications such as
personalized healthcare, finance, and e-commerce. Graph-based RSs play a
crucial role in capturing intricate higher-order interactions among entities.
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.) at the central server. This paper addresses the pervasive
issue of inherent bias within RSs for different demographic groups without
compromising the privacy of sensitive user attributes in FL environment with
the graph-based model. To address the group bias, we propose F2PGNN (Fair
Federated Personalized Graph Neural Network), a novel framework that leverages
the power of Personalized Graph Neural Network (GNN) coupled with fairness
considerations. Additionally, we use differential privacy techniques to fortify
privacy protection. Experimental evaluation on three publicly available
datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47%
- 99% compared to the state-of-the-art while preserving privacy and maintaining
the utility. The results validate the significance of our framework in
achieving equitable and personalized recommendations using GNN within the FL
landscape.
