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障驾驶安全。目前，集中式机器学习是ITS中的主流学习方法，大量交通视频数据分布在分布式边缘设备中。 （例如，智能交通摄像头和行车记录仪）被传输到中央服务器来训练机器学习模型，从而导致效率过高和隐私问题。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来实现协作训练。共享的不幸的是，现有的 FL 包无法完全支持资源有限的设备上的 FL，而这些设备在道路基础设施边缘设备中占主导地位。允许以高效、安全和保护隐私的方式将 FL 部署到 ITS 应用中。拟议的研究将为许多交通应用带来变革性进展，例如自然驾驶研究和交通冲突预测。网络基础设施将得到实际部署。 - 世界交通管理增强交通机构的态势感知和决策能力。拟议的软件工具将是开源的，以增强广泛的 ITS 教育活动的研究基础设施，包括课程开发、学生指导和对 K-12 学生的社区推广。将从效率、安全性和隐私的关键角度建立关于 FL 的新理论和实践结果——这三个属性是在 ITS 中的大量资源有限的道路基础设施边缘设备上广泛采用和部署所必需的。具体来说，(1) 这。项目将以某种方式调查 FL 和(2) 该项目将为 ITS 中数据和模型的恶意攻击提供理论和实践安全工具，以进行实证和认证防御。 (3) 该项目将通过提出新的建议来研究 FL 与交通视频数据中的隐私之间的关系。理论基础设计和 FL 架构，例如隐私保护数据和模型共享 (4) 该项目将与 NVIDIA 一起开发一个真正的分布式测试平台。 Jetson Nano 设备用于测试上述方法，这些小型设备可以部署在 FL 的接线盒和车辆中，以服务于 ITS 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共同资助。刺激竞争性研究 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。