Privacy and Fairness in Federated Learning: on the Perspective of
  Trade-off
Huiqiang Chen Tianqing Zhu Tao Zhang Wanlei Zhou Philip S. Yu
Federated learning (FL) has been a hot topic in recent years. Ever since it
was introduced, researchers have endeavored to devise FL systems that protect
privacy or ensure fair results, with most research focusing on one or the
other. As two crucial ethical notions, the interactions between privacy and
fairness are comparatively less studied. However, since privacy and fairness
compete, considering each in isolation will inevitably come at the cost of the
other. To provide a broad view of these two critical topics, we presented a
detailed literature review of privacy and fairness issues, highlighting unique
challenges posed by FL and solutions in federated settings. We further
systematically surveyed different interactions between privacy and fairness,
trying to reveal how privacy and fairness could affect each other and point out
new research directions in fair and private FL.