From an ethical point of view, concepts such as bias and fairness, while not specific to FL and relevant to most ML applications, can potentially be more challenging topics to address due to the private nature of the distributed datasets and require specific attention. However, as noted before, this challenge comes with the potential benefit to leverage Federated Learning to increase the data diversity.
从伦理的角度来看,诸如偏见和公平之类的概念虽然并非特定于FL而是与大多数ML应用相关,但由于分布式数据集的私有性质,因此有可能成为更具挑战性的主题,需要特别注意。 但是,如前所述,此挑战具有利用联合学习增加数据多样性的潜在好处。