Collaborative Research: CNS Core: Medium: Data-Centric Networks for Distributed Learning

机器学习算法已经彻底改变了许多领域，使他们能够使用历史数据进行预测或检测模式，然后可以用于自动化各种任务并为用户创建新的应用程序。然而，当今许多机器学习应用所需的数据通常是由多个传感器组成的网络收集的。例如，来自智慧城市中环境传感器的数据可以用于预测城市中不同位置的空气污染或交通。使用机器学习算法分析这些数据，然后需要这些设备相互合作，交换数据和模型。该项目设计了设备高效协作的机制。当设备受到异构资源约束时，分布式机器学习算法尤其具有挑战性，例如，具有变化的计算、功率或带宽限制，这在当今的网络中是常见的情况。传统的学习算法要么将所有数据集中到一个位置进行分析，要么将学习算法完全分布到数据源。一种更灵活的方法，而是智能地将数据带到学习算法的计算组件中，反之将计算带到数据源中，可以更好地利用这些设备的资源，但提出了一个自然的问题，即数据和模型组件应该如何通过网络移动。该项目开发了一种以数据为中心的分布式学习方法，利用命名数据网络（NDN）的进步来简化信息交换过程，实现新型分布式学习算法。该项目的成果可能会在大量潜在应用中改进分布式学习，从智能城市到卫星数据分析再到增强现实。该项目还支持目前在教育方面的努力，并扩大代表性不足的社区对计算的参与。这些努力包括（i）开发新的课程材料，向学生传授现实机器学习部署的挑战，（ii）招募高中和本科生从事范围适当的项目，这将有助于实现研究愿景，以及（iii）介绍和指导会议，旨在增加在计算代表性不足的少数民族的参与。卡内基梅隆大学和东北大学。结果，包括算法实现，技术报告和测量数据集，将在CMU托管的存储库中公开提供。该奖项反映了NSF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 algorithms have revolutionized many fields by giving them the ability to use historical data for making predictions or detecting patterns that can then be used to automate various tasks and create new applications for users. The data that many of today’s machine learning applications require, however, is often collected by a network of multiple sensors. For example, data from environmental sensors in smart cities can be used to predict air pollution or traffic at different locations in the city. Analyzing this data with machine learning algorithms then requires these devices to cooperate with each other, exchanging data and models. This project designs mechanisms for devices to efficiently cooperate.Distributing machine learning algorithms is particularly challenging when devices are heterogeneously resource-constrained, e.g., with varying compute, power, or bandwidth limitations, as is often the case in today’s networks. Traditional learning algorithms either bring all data to a single location for analysis, or entirely distribute the learning algorithm to the data sources. A more flexible approach that instead intelligently brings data to the computing components of the learning algorithms, and conversely brings computing to data sources, can better harness these devices’ resources, but raises a natural question of how data and model components should be moved through the network. This project develops a data-centric approach to distributed learning that utilizes advances in Named Data Networking (NDN) to simplify the process of exchanging information, enabling new types of distributed learning algorithms.The outcomes of this project may improve the distributed learning in a vast number of potential applications, ranging from smart cities to satellite data analysis to augmented reality. The project also supports ongoing efforts in education and broadening participation in computing to underrepresented communities. These efforts include (i) development of new course materials that teach students about the challenges of realistic machine learning deployments, (ii) recruitment of high school and undergraduate students to work on suitably scoped projects that will contribute to the research vision, and (iii) presentations and mentoring sessions aimed at increasing the participation of underrepresented minorities in computing.This project is a collaborative effort between Carnegie Mellon University and Northeastern University. Results, including algorithm implementations, technical reports, and measurement datasets, will be made publicly available on a repository hosted by CMU. These will remain available for at least two years after the conclusion of the project.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.