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.

翻译：我们证明，参与联邦学习可能对群体公平性造成损害。事实上，少数参与方对弱势群体（由性别或种族等敏感属性标识）的偏见可能通过网络传播至所有参与方。本文分析并解释了在自然划分的真实世界数据集中联邦学习的偏见传播机制。我们的分析表明，有偏见的参与方会无意但隐蔽地将其偏见编码至少数模型参数中，并在训练过程中持续增强全局模型对敏感属性的依赖。需要特别指出的是，联邦学习中出现的偏见程度高于这些参与方在集中式训练中（使用所有数据联合训练的模型）所遭遇的偏见。这表明偏见源于算法本身。我们的工作呼吁对联邦学习中的群体公平性进行审计，并设计能够抵御偏见传播的鲁棒学习算法。