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.

翻译：联邦学习近年来成为研究热点。自其提出以来，研究者们致力于设计既能保护隐私又能确保结果公平的联邦学习系统，但多数研究仅侧重于其中一方面。作为两个关键的伦理概念，隐私与公平之间的相互作用相对较少被研究。然而，由于隐私与公平存在竞争关系，单独考虑其中一方必然以牺牲另一方为代价。为全面审视这两个重要议题，本文对隐私与公平问题进行了详细的文献综述，重点阐述了联邦学习带来的独特挑战及联邦场景下的解决方案。我们进一步系统梳理了隐私与公平之间的不同交互关系，试图揭示二者如何相互影响，并指出公平且私密的联邦学习中的新研究方向。