RINGS: REALTIME: Resilient Edge-cloud Autonomous Learning with Timely Inferences

机器学习(ML)是新兴实时应用的推动者，从增强现实和智能城市到正在改变人们生活和工作方式的自动驾驶汽车。低延迟对于这些服务至关重要；新兴的实时应用通常需要移动边缘云(MEC)的帮助才能实现实时操作。这一新出现的场景带来了重大的新挑战：移动设备是异构性的，从能量采集传感器到汽车，但存储和计算资源通常有限，通信通常通过低带宽信道进行；训练过的ML模型的实时部署需要适应异质时变本地环境的自主计算和决策；设备需要实时对高维数据做出高精度推断；设备不断收集必须处理、聚合和传递给MEC的新数据；移动用户具有不同的隐私偏好，需要使用MEC进行隐私敏感；移动设备上的应用和服务必须对网络和物理世界的变化具有弹性，以确保个人安全。该项目旨在设计和实验验证一个基于MEC的分布式ML系统。在基于ML的应用程序必须对数据、应用、用户和系统变化具有弹性的实时操作、在线决策和离线训练的背景下，本研究计划从四个方面进行开发和评估：(1)支持移动设备实时推理的以边缘为中心的分布式ML模型和快速分布式半监督训练。(2)基于信息时代的时效性度量，对实时推理方法和系统运行进行了优化，以平衡移动计算和网络资源。(3)MEC辅助ML的实时和在线操作的差异隐私和其他隐私度量正在开发中，并被纳入用于系统自适应的分布式算法。(4)该项目将这些设计方法整合到纽约NSF COSMOS试验台上的概念验证原型中，以验证可行性并评估设备和系统对典型应用的弹性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning (ML) is the enabler of emerging real-time applications ranging from augmented reality and smart cities to autonomous vehicles that are changing how people live and work. Low latency is essential for these services; emerging real-time applications will typically need assistance from a mobile edge cloud (MEC) for real-time operation. This emerging scenario introduces significant new challenges: mobile devices are heterogeneous, ranging from energy-harvesting sensors to automobiles, but storage and compute resources are generally limited and communication is often over low-bandwidth channels; real-time deployment of trained ML models requires autonomous computation and decision-making that is adaptive to heterogeneous time-varying local environments; devices need to make high-accuracy inferences on high-dimensional data in real time; devices continuously gather new data that must be processed, aggregated, and communicated to the MEC; mobile users have heterogenous privacy preferences that require privacy-sensitive use of the MEC; and the applications and services on the mobile devices must be resilient to changes in both the cyber and physical worlds in order to ensure personal safety. This project is aimed at the design and experimental validation of an MEC-based distributed ML system that accounts for these factors.In this setting of real-time operation, online decision-making, and offline training of ML-based applications that must be resilient to data, application, user, and system changes, this research program has four facets: (1) Edge-centric distributed ML models to enable both real-time inferences at mobile devices and fast distributed semi-supervised training are being developed and evaluated. (2) Based on age-of-information timeliness metrics, real-time inference methods and system operation are optimized to balance mobile computation against network resources. (3) Differential privacy and other privacy metrics for real-time and online operation of MEC-assisted ML are being developed and incorporated in the distributed algorithms for system adaptation. (4) The project integrates these design approaches in a proof-of-concept prototype on the NSF COSMOS testbed in NY City to validate feasibility and evaluate device and system resilience for representative applications.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.