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

合作研究：OAC CORE：智能交通系统的联邦学习驱动的交通事件管理

• 批准号: 2313192
• 负责人: Da Yan
• 金额: $ 25万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313192&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; CORE; Federated

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.

智能运输系统（ITS）利用智能交通监视和机器学习（ML）技术来优化交通管理并确保驾驶安全性。目前，集中式ML是ITS中的主流学习方法，在该方法中，分布式边缘设备（例如智能交通摄像机和仪表板）之间的大量流量视频数据被传输到中央服务器以训练ML模型，从而导致了禁止的效率和隐私问题。联合学习（FL）是一个有希望的范式，它利用分布式设备的计算能力，以通过大规模数据对共享ML模型进行协作培训，同时保持数据本地和安全。不幸的是，现有的FL软件包无法在资源有限的设备上完全支持FL，该设备主导了道路基础设施边缘设备。该项目旨在建立一个友好的网络基础结构，以高效，安全和隐私的方式为其应用程序部署FL。拟议的研究将在许多运输应用中带来变革性的进步，例如自然主义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中的交通管理，以增强运输机构的情境意识和决策能力。提出的软件工具将是开源的，以增强其广泛社区的研究基础架构。教育活动包括课程开发，学生的心理和向K-12学生推广。该项目将从效率，安全性和隐私的批判性角度从ITS中的大规模采用和部署进行广泛采用和部署所必需的三种属性，从而为FL建立新的理论和实际结果。具体而言，（1）该项目将系统地研究ITS中FL与不同类型的效率问题之间的相互作用，例如昂贵的计算成本，高通信消耗和较低的设备利用。 （2）该项目将为对ITS中数据和模型的恶意攻击的经验和认证防御提供理论和实用的安全工具。 （3）该项目将通过提出新的理论基础设计和FL架构（例如保护隐私数据和模型共享）来调查流量视频数据中FL与隐私之间的关系。 （4）该项目将使用NVIDIA JETSON NANO设备开发真实的分布式测试台，以测试上述方法。这些小型设备可以部署在佛罗里达州的接线盒和车辆中，以服务其应用。该项目由高级网络基础设施办公室（OAC）核心研究计划和既定计划刺激竞争性研究（EPSCOR）共同资助。该奖项通过评估了NSF的法规诚实，反映了NSF的诚实对构成的构成构成的构想，该奖项已被诚实地构成了基础。