Confidence-Based Federated Distillation for Vision-Based Lane-Centering
Confidence-Based Federated Distillation for Vision-Based Lane-Centering
复制标题
DOI：
10.1109/icasspw59220.2023.10193741
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Yitao Chen;Dawei Chen;Haoxin Wang;Kyungtae Han;Mingbi Zhao
中科院分区：
文献类型：
--
作者：
Yitao Chen;Dawei Chen;Haoxin Wang;Kyungtae Han;Mingbi Zhao
自主驾驶的基本挑战是通过调整转向角度来维持车道的中心。在不共享私人数据的情况下进行协作，但由于数据分布通常是i.d的，因此很难在整个车辆上进行良好的精度。指导全球模型的学习。对基于视觉的车道的全面评估表明，拟议的方法可以分别超过FedAvg和FedDF的11.3％和9％。
A fundamental challenge of autonomous driving is maintaining the vehicle in the center of the lane by adjusting the steering angle. Recent advances leverage deep neural networks to predict steering decisions directly from images captured by the car cameras. Machine learning-based steering angle prediction needs to consider the vehicle’s limitation in uploading large amounts of potentially private data for model training. Federated learning can address these constraints by enabling multiple vehicles to collaboratively train a global model without sharing their private data, but it is difficult to achieve good accuracy as the data distribution is often non-i.i.d. across the vehicles. This paper presents a new confidence-based federated distillation method to improve the performance of federated learning for steering angle prediction. Specifically, it proposes the novel use of entropy to determine the predictive confidence of each local model, and then selects the most confident local model as the teacher to guide the learning of the global model. A comprehensive evaluation of vision-based lane centering shows that the proposed approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.