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

翻译：我们提出FedDrive v2，这是针对自动驾驶中语义分割的联邦学习基准的扩展。第一版旨在研究视觉特征跨客户端域偏移的影响，而本工作则聚焦于标签的分布偏斜性。我们提出了六个新的联邦场景，以探究标签偏斜性如何影响分割模型的性能，并将其与域偏移的影响进行对比。最后，我们研究了在测试过程中利用域信息的影响。官方网站：https://feddrive.github.io