pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Federated learning (FL) algorithms commonly aim to maximize clients' accuracy
by training a model on their collective data. However, in several FL
applications, the model's decisions should meet a group fairness constraint to
be independent of sensitive attributes such as gender or race. While such group
fairness constraints can be incorporated into the objective function of the FL
optimization problem, in this work, we show that such an approach would lead to
suboptimal classification accuracy in an FL setting with heterogeneous client
distributions. To achieve an optimal accuracy-group fairness trade-off, we
propose the Personalized Federated Learning for Client-Level Group Fairness
(pFedFair) framework, where clients locally impose their fairness constraints
over the distributed training process. Leveraging the image embedding models,
we extend the application of pFedFair to computer vision settings, where we
numerically show that pFedFair achieves an optimal group fairness-accuracy
trade-off in heterogeneous FL settings. We present the results of several
numerical experiments on benchmark and synthetic datasets, which highlight the
suboptimality of non-personalized FL algorithms and the improvements made by
the pFedFair method.
