Cooperative perception is essential to enhance the efficiency and safety of
future transportation systems, requiring extensive data sharing among vehicles
on the road, which raises significant privacy concerns. Federated learning
offers a promising solution by enabling data privacy-preserving collaborative
enhancements in perception, decision-making, and planning among connected and
autonomous vehicles (CAVs). However, federated learning is impeded by
significant challenges arising from data heterogeneity across diverse clients,
potentially diminishing model accuracy and prolonging convergence periods. This
study introduces a specialized federated learning framework for CP, termed the
federated dynamic weighted aggregation (FedDWA) algorithm, facilitated by
dynamic adjusting loss (DALoss) function. This framework employs dynamic client
weighting to direct model convergence and integrates a novel loss function that
utilizes Kullback-Leibler divergence (KLD) to counteract the detrimental
effects of non-independently and identically distributed (Non-IID) and
unbalanced data. Utilizing the BEV transformer as the primary model, our
rigorous testing on the OpenV2V dataset, augmented with FedBEVT data,
demonstrates significant improvements in the average intersection over union
(IoU). These results highlight the substantial potential of our federated
learning framework to address data heterogeneity challenges in CP, thereby
enhancing the accuracy of environmental perception models and facilitating more
robust and efficient collaborative learning solutions in the transportation
sector.

合作感知和联邦学习在未来交通系统中具有很大潜力，但数据异质性是一个重要挑战。本研究介绍了一种专门用于合作感知的联邦学习框架，名为 FedDWA 算法，并结合动态调节损失函数。实验证明该框架能够显著改善环境感知模型的准确性，提高交通部门的协同学习解决方案。