mlsanityData to 7 Oct 2026

No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

arXiv 2312.10080 ↗First posted 10 Dec 2023cs.IR cs.AI cs.LGcited by 18

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Abstract (arXiv)

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 10 Dec 2023.

Builds on

18 of 35 references.

References · 18

  1. 2017Graph Attention Networkscited by 8,854
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  7. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  8. 2019Neural Graph Collaborative Filteringcited by 660
  9. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  10. 2019Secure Federated Matrix Factorizationcited by 102
  11. 2022A Survey on the Fairness of Recommender Systemscited by 102
  12. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
6 more
  1. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
  2. 2020Fairness-aware Agnostic Federated Learningcited by 87
  3. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  4. 2021Federated Learning with Fair Averagingcited by 59
  5. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  6. 2021FAIR: Fairness-Aware Information Retrieval Evaluationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 3.

18 papers.

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  1. 2025GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphscited by 3
  2. 2025FedCF: Fair Federated Conformal Predictioncited by 3
  3. 2024GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networkscited by 2
  4. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  5. 2024BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocationcited by 1
  6. 2025MAPN: Enhancing Heterogeneous Sparse Graph Representation by Mamba-based Asynchronous Aggregationcited by 1
  7. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  8. 2025Towards Unbiased Federated Graph Learning: Label and Topology Perspectivescited by 1
  9. 2025FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRAcited by 1
  10. 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0
  11. 2024Enhancing Robustness of Graph Neural Networks through p-Laplaciancited by 0
  12. 2024Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendationcited by 0
6 more
  1. 2025FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-pathscited by 0
  2. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  3. 2025Enhancing Robustness of Graph Neural Networks through p-Laplaciancited by 0
  4. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  5. 2026Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learningcited by 0
  6. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0