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Federated Learning (FL) is a novel privacy-protection distributed machinelearning paradigm that guarantees user privacy and prevents the risk of dataleakage due to the advantage of the client's local training. Researchers havestruggled to design fair FL systems that ensure fairness of results. However,the interplay between fairness and privacy has been less studied. Increasingthe fairness of FL systems can have an impact on user privacy, while anincrease 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 protectthe privacy of the client model, we propose a privacy-protection fairness FLmethod. The results show that the accuracy of the fair model with privacyincreases because privacy breaks the constraints of the fairness metrics. Inour experiments, we conclude the relationship between privacy, fairness andutility, and there is a tradeoff between these.

Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning