  Federated Learning (FL) is a distributed machine learning framework in which
a set of local communities collaboratively learn a shared global model while
retaining all training data locally within each community. Two notions of
fairness have recently emerged as important issues for federated learning:
group fairness and community fairness. Group fairness requires that a model's
decisions do not favor any particular group based on a set of legally protected
attributes such as race or gender. Community fairness requires that global
models exhibit similar levels of performance (accuracy) across all
collaborating communities. Both fairness concepts can coexist within an FL
framework, but the existing literature has focused on either one concept or the
other. This paper proposes and analyzes a post-processing fair federated
learning (FFL) framework called post-FFL. Post-FFL uses a linear program to
simultaneously enforce group and community fairness while maximizing the
utility of the global model. Because Post-FFL is a post-processing approach, it
can be used with existing FL training pipelines whose convergence properties
are well understood. This paper uses post-FFL on real-world datasets to mimic
how hospital networks, for example, use federated learning to deliver community
health care. Theoretical results bound the accuracy lost when post-FFL enforces
both notion of fairness. Experimental results illustrate that post-FFL
simultaneously improves both group and community fairness in FL. Moreover,
post-FFL outperforms the existing in-processing fair federated learning in
terms of improving both notions of fairness, communication efficiency and
computation cost.
