WassFFed: Wasserstein Fair Federated Learning

TL;DR

This paper introduces WassFFed, a novel federated learning framework that uses Wasserstein barycenters to enhance fairness across diverse user groups while maintaining accuracy, addressing key challenges of non-IID data and surrogate function inconsistencies.

Contribution

WassFFed is the first framework to apply Wasserstein barycenters for fairness in federated learning, effectively aligning local and global model outputs across heterogeneous data distributions.

Findings

WassFFed outperforms existing methods in fairness and accuracy on real-world datasets.

The approach effectively handles non-IID data distributions among clients.

Experimental results show improved fairness without sacrificing model performance.

Abstract

Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically distributed among diverse user groups. Existing research on fairness predominantly assumes access to the entire training data, making direct transfer to FL challenging. However, the limited existing research on fairness in FL does not effectively address two key challenges, i.e., (CH1) Current methods fail to deal with the inconsistency between fair optimization results obtained with surrogate functions and fair classification results. (CH2) Directly aggregating local fair models does not always yield a globally fair model due to non Identical and Independent data Distributions (non-IID) among clients. To address these challenges, we propose a Wasserstein Fair Federated Learning framework, namely WassFFed. To tackle CH1, we ensure that the outputs of local models, rather than the loss calculated with surrogate functions or classification results with a threshold, remain independent of various user groups. To resolve CH2, we employ a Wasserstein barycenter calculation of all local models' outputs for each user group, bringing local model outputs closer to the global output distribution to ensure consistency between the global model and local models. We conduct extensive experiments on three real-world datasets, demonstrating that WassFFed outperforms existing approaches in striking a balance between accuracy and fairness.