Federated Learning(FL)has experienced rapid development due to its advantages in distributed structure and privacy security.However,the fairness issues caused by large-scale FL affect the sustainability of FL systems.In response to the fairness issues in FL,recent researches on fairness in FL was reviewed systematically and analyzed deeply.Firstly,the workflow and definitions of FL were explained,and biases and fairness concepts in FL were summarized.Secondly,commonly used datasets in fairness research of FL were detailed,and the challenges faced by fairness research were discussed.Finally,the advantages,disadvantages,applicable scenarios,and experimental setting of relevant research work were summed up from four aspects:data source selection,model optimization,contribution evaluation,and incentive mechanism,and the future research directions and trends in fairness of FL were prospected.
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