FairGFL: Fairness-Aware Federated Learning with Overlapping Subgraphs
Research Paper#Federated Learning, Graph Neural Networks, Fairness, Privacy🔬 Research|Analyzed: Jan 3, 2026 19:04•
Published: Dec 29, 2025 06:31
•1 min read
•ArXivAnalysis
This paper addresses the fairness issue in graph federated learning (GFL) caused by imbalanced overlapping subgraphs across clients. It's significant because it identifies a potential source of bias in GFL, a privacy-preserving technique, and proposes a solution (FairGFL) to mitigate it. The focus on fairness within a privacy-preserving context is a valuable contribution, especially as federated learning becomes more widespread.
Key Takeaways
- •Identifies and addresses the fairness issue arising from imbalanced overlapping subgraphs in graph federated learning.
- •Proposes FairGFL, a novel algorithm that enhances cross-client fairness while maintaining model utility in a privacy-preserving manner.
- •Employs a weighted aggregation approach and a carefully crafted regularizer to improve fairness and model utility.
- •Demonstrates superior performance of FairGFL compared to baseline algorithms on benchmark graph datasets.
Reference / Citation
View Original"FairGFL incorporates an interpretable weighted aggregation approach to enhance fairness across clients, leveraging privacy-preserving estimation of their overlapping ratios."