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

RINGS：实时：具有及时推理能力的弹性边缘云自主学习

• 批准号: 2148104
• 负责人: Anand Sarwate
• 金额: $ 100万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148104&HistoricalAwards=false
• 关键词: RINGS; REALTIME; Resilient; Edge; cloud

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.

机器学习（ML）是新兴实时应用程序的推动者，从增强现实和智能城市到改变人们生活和工作方式的自动驾驶汽车。 低潜伏期对于这些服务至关重要；新兴的实时应用程序通常需要移动边缘云（MEC）的帮助进行实时操作。这种新兴方案引入了重大的新挑战：移动设备是异质的，从能量收获的传感器到汽车，但是存储和计算资源通常受到限制，并且通信通常在低频道的渠道上； 训练有素的ML模型的实时部署需要自主计算和决策，这些计算和决策适应异构时间变化的本地环境；设备需要实时对高维数据进行高准确性推断；设备不断收集必须处理，汇总和传达给MEC的新数据；移动用户具有需要对MEC隐私敏感使用的异质隐私首选项；为了确保人身安全，移动设备上的应用程序和服务必须对网络和物理世界的变化有弹性。 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评估。 （2）基于信息年龄及时度量，实时推理方法和系统操作被优化，以平衡移动计算与网络资源。 （3）正在开发并纳入用于系统适应的分布式算法中的实时和在线操作的差异隐私和其他隐私指标。 （4）该项目将这些设计方法整合到纽约市NSF Cosmos测试的概念验证原型中，以验证可行性并评估对代表性应用程序的设备和系统的弹性。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务。

项目成果

Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis

• 发表时间: 2020-01
• 期刊: Trans. Mach. Learn. Res.
• 作者: Haroon Raja;W. Bajwa

FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis

• DOI: 10.1109/tsp.2022.3229635
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Arpita Gang;W. Bajwa

C-DIEGO: An Algorithm with Near-Optimal Sample Complexity for Distributed, Streaming PCA

• DOI: 10.1109/ciss56502.2023.10089668
• 发表时间: 2023-03
• 期刊: 2023 57th Annual Conference on Information Sciences and Systems (CISS)
• 作者: Muhammad Zulqarnain;Arpita Gang;W. Bajwa

Exit Time Analysis for Approximations of Gradient Descent Trajectories Around Saddle Points

• DOI: 10.1093/imaiai/iaac025
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Rishabh Dixit;W. Bajwa

Privacy Leakage in Discrete-Time Updating Systems

离散时间更新系统中的隐私泄露

• DOI: 10.1109/isit50566.2022.9834673
• 发表时间: 2022
• 期刊: 2022 IEEE International Symposium on Information Theory (ISIT
• 作者: Sathyavageeswaran, Nitya;Yates, Roy D.;Sarwate, Anand D.;Mandayam, Narayan
• 通讯作者: Mandayam, Narayan