Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering

TL;DR

This paper introduces clustering-based federated learning algorithms that balance local accuracy and fairness in heterogeneous environments, outperforming existing methods without explicit fairness constraints.

Contribution

It proposes novel FL algorithms that incorporate fairness metrics into clustering, enabling a tunable trade-off between personalization and fairness in heterogeneous settings.

Findings

Methods match or exceed existing locally fair FL performance.

Personalization can inherently improve local fairness.

Algorithms effectively balance accuracy and fairness without explicit fairness interventions.

Abstract

Federated Learning (FL) has been a pivotal paradigm for collaborative training of machine learning models across distributed datasets. In heterogeneous settings, it has been observed that a single shared FL model can lead to low local accuracy, motivating personalized FL algorithms. In parallel, fair FL algorithms have been proposed to enforce group fairness on the global models. Again, in heterogeneous settings, global and local fairness do not necessarily align, motivating the recent literature on locally fair FL. In this paper, we propose new FL algorithms for heterogeneous settings, spanning the space between personalized and locally fair FL. Building on existing clustering-based personalized FL methods, we incorporate a new fairness metric into cluster assignment, enabling a tunable balance between local accuracy and fairness. Our methods match or exceed the performance of existing locally fair FL approaches, without explicit fairness intervention. We further demonstrate (numerically and analytically) that personalization alone can improve local fairness and that our methods exploit this alignment when present.