We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagation in federated learning on naturally partitioned real-world datasets. Our analysis reveals that biased parties unintentionally yet stealthily encode their bias in a small number of model parameters, and throughout the training, they steadily increase the dependence of the global model on sensitive attributes. What is important to highlight is that the experienced bias in federated learning is higher than what parties would otherwise encounter in centralized training with a model trained on the union of all their data. This indicates that the bias is due to the algorithm. Our work calls for auditing group fairness in federated learning and designing learning algorithms that are robust to bias propagation.

参与联邦学习可能对群体公平性产生不利影响，而且这种偏见通过网络传播给所有参与方；我们分析并解释了联邦学习中的偏见传播问题，并发现在训练过程中，有偏见的参与方会不知不觉地在一小部分模型参数中编码其偏见，增加全局模型对敏感属性的依赖性；与集中式训练相比，联邦学习中的偏见更严重，这表明偏见源于算法；我们的工作呼吁在联邦学习中审计群体公平性，并设计对偏见传播具有强韧性的学习算法。

联邦学习中的偏差传播