  Bird's eye view (BEV) perception is becoming increasingly important in the
field of autonomous driving. It uses multi-view camera data to learn a
transformer model that directly projects the perception of the road environment
onto the BEV perspective. However, training a transformer model often requires
a large amount of data, and as camera data for road traffic are often private,
they are typically not shared. Federated learning offers a solution that
enables clients to collaborate and train models without exchanging data but
model parameters. In this paper, we introduce FedBEVT, a federated transformer
learning approach for BEV perception. In order to address two common data
heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying
sensor numbers in perception systems, we propose two approaches -- Federated
Learning with Camera-Attentive Personalization (FedCaP) and Adaptive
Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world
settings, we create a dataset consisting of four typical federated use cases.
Our findings suggest that FedBEVT outperforms the baseline approaches in all
four use cases, demonstrating the potential of our approach for improving BEV
perception in autonomous driving.

Computer Vision and Pattern Recognition