GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning

• URL: http://arxiv.org/abs/2401.03562v2
• Date: Wed, 02 Oct 2024 21:13:27 GMT
• Title: GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
• Authors: Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang, Jian Liu,
• Abstract summary: Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. FL tends to be biased against certain demographic groups due to the inherent FL properties, such as data heterogeneity and party selection. We propose GFAIR, a client-server codesign that can improve global and local group fairness without the need for sensitive statistics about the client's private datasets.
• Score: 8.033939709734451
• License:
• Abstract: Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federated learned model tends to be biased against certain demographic groups (e.g., racial and gender groups) due to the inherent FL properties, such as data heterogeneity and party selection. Unlike centralized learning, mitigating bias in FL is particularly challenging as private training datasets and their sensitive attributes are typically not directly accessible. Most prior research in this field only focuses on global fairness while overlooking the local fairness of individual clients. Moreover, existing methods often require sensitive information about the client's local datasets to be shared, which is not desirable. To address these issues, we propose GLOCALFAIR, a client-server co-design fairness framework that can jointly improve global and local group fairness in FL without the need for sensitive statistics about the client's private datasets. Specifically, we utilize constrained optimization to enforce local fairness on the client side and adopt a fairness-aware clustering-based aggregation on the server to further ensure the global model fairness across different sensitive groups while maintaining high utility. Experiments on two image datasets and one tabular dataset with various state-of-the-art fairness baselines show that GLOCALFAIR can achieve enhanced fairness under both global and local data distributions while maintaining a good level of utility and client fairness.

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