Collaborative Research: OAC CORE: Federated-Learning-Driven Traffic Event Management for Intelligent Transportation Systems

智能交通系统（ITS）利用智能交通监控和机器学习（ML）技术来优化交通管理并保证驾驶安全。目前，集中式ML是ITS中的主流学习方法，其中分布式边缘设备（例如，智能交通摄像头和仪表盘摄像头）被传输到中央服务器以训练ML模型，导致效率和隐私问题。联邦学习（FL）是一种很有前途的范例，它利用分布式设备的计算能力，在大规模数据上实现共享ML模型的协作训练，同时保持数据的本地和安全。不幸的是，现有的FL包不能完全支持资源有限的设备上的FL，这主导了道路基础设施的边缘设备。该项目旨在建立一个边缘友好的网络基础设施，使FL能够以高效，安全和隐私保护的方式部署到ITS应用程序中。这项研究将为许多交通应用带来变革性的进步，例如自然驾驶研究和交通冲突预测。拟议的网络基础设施将用于现实世界的交通管理，以提高运输机构的态势感知和决策能力。拟议的软件工具将是开源的，以加强广泛的ITS社区的研究基础设施。教育活动包括课程开发、学生指导和K-12学生的推广。该项目将从效率、安全和隐私的关键角度建立关于FL的新的理论和实践结果-这三个属性是ITS中大规模资源有限的道路基础设施边缘设备广泛采用和部署所必需的。具体而言，（1）系统地研究FL与ITS中计算成本高、通信消耗高、设备利用率低等不同类型的效率问题之间的相互影响。(2)该项目将提供理论和实践的安全工具，用于经验和认证防御ITS中的数据和模型的恶意攻击。(3)该项目将通过提出新的理论基础设计和FL架构（如隐私保护数据和模型共享）来研究FL与交通视频数据中隐私之间的关系。(4)该项目将开发一个真实的分布式测试平台，使用NVIDIA Jetson Nano设备来测试上述方法。这些小型设备可以部署在接线盒和车辆FL服务ITS的应用程序。该项目是由高级网络基础设施办公室（OAC）的核心研究计划和既定计划，以刺激竞争力的研究（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Intelligent transportation systems (ITS) utilize smart traffic surveillance and machine learning (ML) technologies to optimize traffic management and guarantee driving safety. Currently, centralized ML is the mainstream learning method in the ITS, where vast amounts of traffic video data among distributed edge devices (e.g., smart traffic cameras and dashcams) are transmitted to a central server to train an ML model, resulting in prohibitive efficiency and privacy concerns. Federated learning (FL) is a promising paradigm that leverages the computing power of distributed devices to enable collaborative training of shared ML models over large-scale data while keeping the data local and safe. Unfortunately, existing FL packages fail to fully support the FL on resource-limited devices, which dominate the road infrastructure edge devices. This project aims to build an edge-friendly cyberinfrastructure that allows FL to be deployed for ITS applications in an efficient, secure, and privacy-preserving manner. The proposed research will bring transformative advances in many transportation applications, such as naturalistic driving study and traffic conflict prediction. The proposed cyberinfrastructure will be deployed for real-world traffic management to enhance transportation agencies’ situational awareness and decision-making capabilities. The proposed software tools will be open source to enhance the research infrastructure for the broad ITS communities. Educational activities include curriculum development, student mentoring, and outreach to K-12 students.The project will establish new theoretical and practical results about the FL from the critical perspectives of efficiency, security, and privacy — three properties necessary for broad adoption and deployment on the massive resource-limited road infrastructure edge devices in the ITS. Specifically, (1) this project will systematically investigate the interplay between the FL and distinct types of efficiency issues in the ITS, such as expensive computation cost, high communication consumption, and low device utilization. (2) This project will provide theoretical and practical security tools for both empirical and certified defenses against malicious attacks on data and models in the ITS. (3) This project will investigate the relationship between FL and privacy in the traffic video data by proposing new theoretically grounded designs and FL architectures, such as privacy-preserving data and model sharing. (4) This project will develop a real distributed testbed with NVIDIA Jetson Nano devices to test the above-proposed methods. These small devices can be deployed in junction boxes and vehicles for FL to serve ITS applications.This project is jointly funded by the Office of Advanced Cyberinfrastructure (OAC) Core Research program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.