No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

TL;DR

This paper introduces F2PGNN, a federated graph neural network framework that enhances fairness and privacy in personalized recommendation systems without exposing sensitive user data.

Contribution

It proposes a novel federated GNN model with fairness constraints and differential privacy, addressing bias and privacy issues in graph-based recommendation systems.

Findings

Mitigates group unfairness by 47% - 99%

Preserves user privacy with differential privacy techniques

Maintains recommendation utility while improving fairness

Abstract

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.