解决自动驾驶车辆联邦学习中的数据异质性问题，提升分类准确率。
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
- 分区域层级聚合，按距离动态划分子区域进行协同训练。
- 在三个真实数据集上准确率提升至少3.69%。
- 适合异构交通环境下的自动驾驶模型联合训练。
新兴的联邦学习使分布式自动驾驶车辆能够在不暴露原始数据的情况下协同训练深度学习模型，为利用快速增长的自动驾驶数据提供了巨大潜力。然而，由于交通环境复杂、驾驶场景多样，将联邦学习应用于自动驾驶车辆时不可避免地面临车辆数据非独立同分布（Non-IID）的挑战，可能导致收敛失败和训练精度低下。本文提出一种新型的分层联邦区域学习框架（FedRAV），该框架为两阶段设计，根据定义的区域距离自适应地将包含车辆的大区域划分为子区域，并实现个性化车载模型与区域模型的协同优化。该方法确保个性化车载模型能够保留有益模型并舍弃无益模型。我们在三种真实自动驾驶数据集上，在多种异构设置下验证了所提框架相较于现有联邦学习算法的有效性。实验结果表明，该框架性能更优，准确率提升至少3.69%。FedRAV源代码已公开：https://github.com/yjzhai-cs/FedRAV。
原文摘要 · Abstract (English)
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV), a two-stage framework, which adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. This approach ensures that the personalized vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on three real-world autonomous driving datasets in various heterogeneous settings. The experiment results demonstrate that our framework outperforms those known algorithms, and improves the accuracy by at least 3.69%. The source code of FedRAV is available at: https://github.com/yjzhai-cs/FedRAV.
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