Eid Kishawy Mohab M, Abd El-Hafez Mohamed T, Yousri Retaj, Darweesh M Saeed
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, L8S 4K1, Canada.
School of Engineering and Applied Sciences, Nile University, Giza, 12677, Egypt.
Sci Rep. 2024 Oct 23;14(1):25029. doi: 10.1038/s41598-024-71187-8.
Autonomous Vehicles (AV) is one of the most evolving industries in the last decade. However, one of the bottlenecks of this evolution is providing data that contains different scenarios and scenes to improve the models without exposing the privacy and security of the edge vehicles. The authors of this research propose a secure and efficient novel solution for lane segmentation in AVs through the use of Federated Learning (FL). FedLane involves initial training of U-Net, ResUNet, and ResUNet++ models, followed by real-time inference in edge devices and the application of FL to update the server model using clients' data. The study found that FL has enhanced the performance of lane segmentation significantly over baseline, enabling decentralized privacy-preserving collaborative optimization with increased dice coef from 0.9429 to 0.9794 for U-Net, from 0.9291 to 0.9854 for ResUNet and from 0.9079 to 0.9675 for ResUNet++. Additionally, the models show increased stability over the training iterations, highlighting the potential of FL to play a significant role in the future of automation in the AV industry.
自动驾驶汽车（AV）是过去十年中发展最为迅速的行业之一。然而，这一发展过程中的瓶颈之一是，在不暴露边缘车辆的隐私和安全的情况下，提供包含不同场景的数据以改进模型。本研究的作者提出了一种通过使用联邦学习（FL）实现自动驾驶汽车车道分割的安全高效的新颖解决方案。FedLane包括对U-Net、ResUNet和ResUNet++模型进行初始训练，然后在边缘设备中进行实时推理，并应用联邦学习使用客户端数据更新服务器模型。研究发现，与基线相比，联邦学习显著提高了车道分割的性能，实现了去中心化的隐私保护协作优化，U-Net的骰子系数从0.9429提高到0.9794，ResUNet从0.9291提高到0.9854，ResUNet++从0.9079提高到0.9675。此外，这些模型在训练迭代过程中表现出更高的稳定性，凸显了联邦学习在自动驾驶汽车行业未来自动化中发挥重要作用的潜力。
