  We propose FedDrive v2, an extension of the Federated Learning benchmark for
Semantic Segmentation in Autonomous Driving. While the first version aims at
studying the effect of domain shift of the visual features across clients, in
this work, we focus on the distribution skewness of the labels. We propose six
new federated scenarios to investigate how label skewness affects the
performance of segmentation models and compare it with the effect of domain
shift. Finally, we study the impact of using the domain information during
testing. Official website: https://feddrive.github.io
