Fairness and privacy are both significant social norms in machine learning. In (Hu et al
2019), we propose a distributed framework to learn fair prediction models while protecting …

Fair machine learning has become a significant research topic with broad societal impact.
However, most fair learning methods require direct access to personal demographic data …

With the rising concerns over privacy and fairness in machine learning, privacy-preserving
fair machine learning has received tremendous attention in recent years. However, most …

Deploying machine learning (ML) models often requires both fairness and privacy
guarantees. Both of these objectives present unique trade-offs with the utility (eg, accuracy) …

Motivated by settings in which predictive models may be required to be non-discriminatory
with respect to certain attributes (such as race), but even collecting the sensitive attribute …

Machine learning models have demonstrated promising performance in many areas.
However, the concerns that they can be biased against specific demographic groups hinder …

Building fair machine learning models becomes more and more important. As many
powerful models are built by collaboration among multiple parties, each holding some …

Federated learning (FL) is a new machine learning paradigm to overcome the challenge of
data silos and has garnered significant attention. However, through our observations, a …

Federated learning is a machine learning framework where many clients (eg mobile devices
or whole organizations) collaboratively train a model under the orchestration of a central …

Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair
machine learning aims at minimizing discrimination against protected groups by, for …