Towards Fair Federated Learning
Zirui Zhou, Lingyang Chu, Changxin Liu, Lanjun Wang, Jian Pei, Yong Zhang
Abstract
Federated learning has become increasingly popular as it facilitates collaborative training of machine learning models among multiple clients while preserving their data privacy. In practice, one major challenge for federated learning is to achieve fairness in collaboration among the participating clients, because different clients' contributions to a model are usually far from equal due to various reasons. Besides, as machine learning models are deployed in more and more important applications, how to achieve model fairness, that is, to ensure that a trained model has no discrimination against sensitive attributes, has become another critical desiderata for federated learning. In this tutorial, we discuss formulations and methods such that collaborative fairness, model fairness, and privacy can be fully respected in federated learning. We review the existing efforts and the latest progress, and discuss a series of potential directions.