Toward the Tradeoffs between Privacy, Fairness and Utility in Federated
  Learning
Kangkang Sun Xiaojin Zhang Xi Lin Gaolei Li Jing Wang Jianhua Li
Federated Learning (FL) is a novel privacy-protection distributed machine
learning paradigm that guarantees user privacy and prevents the risk of data
leakage due to the advantage of the client's local training. Researchers have
struggled to design fair FL systems that ensure fairness of results. However,
the interplay between fairness and privacy has been less studied. Increasing
the fairness of FL systems can have an impact on user privacy, while an
increase in user privacy can affect fairness. In this work, on the client side,
we use fairness metrics, such as Demographic Parity (DemP), Equalized Odds
(EOs), and Disparate Impact (DI), to construct the local fair model. To protect
the privacy of the client model, we propose a privacy-protection fairness FL
method. The results show that the accuracy of the fair model with privacy
increases because privacy breaks the constraints of the fairness metrics. In
our experiments, we conclude the relationship between privacy, fairness and
utility, and there is a tradeoff between these.