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自动驾驶汽车，这些应用正在改变人们的生活和工作方式。低延迟对这些服务至关重要；新兴的实时应用程序通常需要移动边缘云（MEC）的帮助来实现实时操作。这一新兴场景带来了重大的新挑战：移动设备是异构的，从能量收集传感器到汽车，但存储和计算资源通常有限，通信通常通过低带宽信道；训练好的机器学习模型的实时部署需要能够适应异构时变局部环境的自主计算和决策；设备需要实时对高维数据进行高精度推断；设备不断收集新数据，这些数据必须进行处理、汇总并传达给MEC；移动用户具有不同的隐私偏好，需要对MEC的隐私敏感使用；移动设备上的应用程序和服务必须能够适应网络和物理世界的变化，以确保人身安全。本项目旨在设计和实验验证一个基于mec的分布式ML系统，该系统考虑了这些因素。在这种实时操作、在线决策和基于机器学习的应用程序离线训练的背景下，必须对数据、应用程序、用户和系统变化具有弹性，本研究计划有四个方面：(1)正在开发和评估以边缘为中心的分布式机器学习模型，以实现移动设备上的实时推断和快速分布式半监督训练。(2)基于信息时代时效性指标，优化实时推理方法和系统运行，实现移动计算与网络资源的平衡。(3)正在开发用于mec辅助ML实时和在线操作的差分隐私和其他隐私指标，并将其纳入分布式算法以进行系统适应。(4)该项目将这些设计方法集成到纽约市NSF COSMOS试验台的概念验证原型中，以验证可行性并评估代表性应用的设备和系统弹性。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。

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.