Bias Propagation in Federated Learning
Hongyan Chang Reza Shokri
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.