III: Small: Opportunistic Learning on Wheels: Peer-wise Training of Machine Learning Models among Connected Vehicles

车辆正在成为车轮上的计算机，配备了各种各样的传感器，为驾驶控制、道路和天气状况产生大量复杂的数据。机器学习模型对于车辆解释数据、识别环境和做出控制决策至关重要。由于车辆不断移动，在车辆环境中训练稳健、准确的模型提出了新的挑战；低速率、不可靠、间歇的对等通信和云通信；以及各种各样的道路和天气条件。最近的联邦学习方法已经开始解决受约束的云通信问题。然而，他们没有利用资源丰富的车辆之间的对等协作，也没有考虑数据质量、数量和多样性的时空相关性，因此无法充分利用所产生的丰富数据集。该项目特别利用车辆环境中的独特机会，使用丰富的数据集训练稳健、准确的模型。它将支持本科生和研究生在现代机器学习方法这一重要学科上的教育和培训，指导高中生进行科学探索，与业界交流最新进展，并可能进行技术转让的试点研究。该项目将创造一种新的机会学习（OL）模式：对等邻近车辆利用相遇的机会性、连接性和数据可用性，并利用这些来协调对等协作和云协助，共同训练强大、准确的机器学习模型。附近的车辆相互交换训练更新，并与云生成聚合模型。基于车辆的短期/长期移动性和连通性，车辆和路边盒子迁移或预取训练状态，以处理车辆的出发和到达。从驾驶员成本分布的探索-利用学习中获得的激励机制将激励车辆参与，在保护驾驶员隐私的同时最大化培训效用。该项目将产生一套完整的基础算法和系统，用于在高度动态的车辆和边缘环境中训练准确、健壮的机器学习模型，超出现有联邦学习方法的能力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Vehicles are becoming computers on wheels, instrumented with rich varieties of sensors producing large amounts of complex data for driving control, road and weather conditions. Machine learning models become indispensable to enable the vehicles to interpret the data, recognize the environments and make control decisions. Training robust, accurate models in vehicular environments pose new challenges due to constant vehicle mobility; low-rate, unreliable, intermittent peer-wise and cloud communication; and extremely diverse road/weather conditions. Recent federated learning methods have started to address constrained cloud communication. However, they do not exploit peer collaboration among resource-rich vehicles, or consider spatial-temporal correlation in data quality, quantity and diversity, thus cannot fully utilize the rich datasets produced. This project specifically exploits unique opportunities in vehicular environments to training robust, accurate models using rich datasets. It will support the education and training of both undergraduate and graduate students in the important discipline of modern machine learning methods, the mentoring of high school students in scientific exploration, and exchange of latest advances with the industry and possible pilot studies for technology transfer.This project will create a novel opportunistic learning (OL) paradigm: peer-wise vicinity vehicles exploit the opportunistic nature of encounters, connectivity and data availability, and use that to orchestrate peer collaboration and cloud assistance to train robust, accurate machine learning models jointly. Vicinity vehicles exchange training updates peer-wise and with the cloud to produce aggregate models. Based on short/long term vehicular mobility and connectivity, vehicles and road-side boxes migrate or pre-fetch training states to handle departure and arrival of vehicles. Incentive mechanisms derived from explore-exploit learning of drivers' cost distributions will motivate vehicle participation, maximize training utility while preserving driver privacy. This project will produce a holistic set of foundational algorithms and systems for training accurate, robust machine learning models in highly dynamic vehicular and edge environments beyond what existing federated learning methods can achieve.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.