The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and intelligent transportation systems (ITS), by enabling low-latency big data processing in a dense interconnected network that comprises vehicles, infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily reliant on machine learning (ML) and can strongly benefit from the wealth of sensory data generated at the edge, which calls for measures to reconcile model training with preserving the privacy of sensitive user data. Federated learning (FL) stands out as a promising solution to train sophisticated ML models in vehicular networks while protecting the privacy of road users and mitigating communication overhead. This paper examines the federated optimization of the cutting-edge YOLOv7 model to tackle real-time object detection amid data heterogeneity, encompassing unbalancedness, concept drift, and label distribution skews. To this end, we introduce FedPylot, a lightweight MPI-based prototype to simulate federated object detection experiments on high-performance computing (HPC) systems, where we safeguard server-client communications using hybrid encryption. Our study factors in accuracy, communication cost, and inference speed, thereby presenting a balanced approach to the challenges faced by autonomous vehicles. We demonstrate promising results for the applicability of FL in IoV and hope that FedPylot will provide a basis for future research into federated real-time object detection. The source code is available at https://github.com/cyprienquemeneur/fedpylot.

翻译：车联网（IoV）作为自动驾驶和智能交通系统（ITS）的关键组成部分，通过构建由车辆、基础设施、行人及云端构成的密集互联网络，实现低延迟大数据处理。自动驾驶车辆高度依赖机器学习（ML），并可显著受益于边缘端产生的丰富传感数据，这要求协调模型训练与敏感用户数据隐私保护之间的关系。联邦学习（FL）成为一种有前景的解决方案，可在保护道路用户隐私并降低通信开销的同时，在车辆网络中训练复杂的ML模型。本文研究针对数据异质性（包括不平衡性、概念漂移和标签分布偏斜）下实时目标检测的最先进YOLOv7模型的联邦优化。为此，我们提出FedPylot——一种轻量级基于MPI的原型系统，用于在高性能计算（HPC）系统上模拟联邦目标检测实验，并通过混合加密保障服务器-客户端通信安全。本研究综合考虑精度、通信成本和推理速度，为自动驾驶车辆面临的挑战提供均衡解决方案。我们展示了FL在IoV中应用的良好结果，并期望FedPylot能为未来联邦实时目标检测研究奠定基础。源代码可从https://github.com/cyprienquemeneur/fedpylot获取。