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，配备了丰富的传感器，为驾驶控制、道路和天气状况提供大量复杂的数据。机器学习模型变得不可或缺，使车辆能够解释数据，识别环境并做出控制决策。在车辆环境中训练强大、准确的模型带来了新的挑战，因为车辆的移动性很大;低速率、不可靠、间歇性的点对点和云通信;以及极其多样化的道路/天气条件。最近的联邦学习方法已经开始解决受限的云通信问题。然而，他们没有利用资源丰富的车辆之间的同行合作，或考虑时空相关性的数据质量，数量和多样性，从而不能充分利用丰富的数据集产生。该项目专门利用车辆环境中的独特机会，使用丰富的数据集训练强大，准确的模型。它将支持现代机器学习方法这一重要学科的本科生和研究生的教育和培训，指导高中生进行科学探索，并与业界交流最新进展和可能的技术转让试点研究。该项目将创建一个新的机会主义学习（OL）范式：对等邻近车辆利用相遇、连接和数据可用性的机会主义性质，并使用其来协调对等协作和云协助，以联合训练鲁棒、准确的机器学习模型。邻近车辆以对等方式交换培训更新，并与云一起生成聚合模型。基于短期/长期车辆移动性和连接性，车辆和路边盒迁移或预取训练状态以处理车辆的出发和到达。驾驶员成本分布的探索-利用学习的激励机制将激励车辆参与，最大化培训效用，同时保护驾驶员隐私。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，强大的机器学习模型，超出现有的联邦学习方法所能实现的范围。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.