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

合作研究：CNS 核心：媒介：用于分布式学习的以数据为中心的网络

• 批准号: 2106891
• 负责人: Carlee Joe-Wong
• 金额: $ 50.15万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106891&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

机器学习算法使许多领域发生了革命性的变化，使它们能够使用历史数据进行预测或检测模式，然后这些模式可以用于自动执行各种任务，并为用户创建新的应用程序。然而，今天的许多机器学习应用程序所需的数据通常是由多个传感器组成的网络收集的。例如，智能城市中环境传感器的数据可以用来预测城市不同地点的空气污染或交通。然后，用机器学习算法分析这些数据需要这些设备相互协作，交换数据和模型。这个项目为设备设计了有效协作的机制。当设备是异质资源受限时，例如，具有不同的计算、功率或带宽限制时，分布式机器学习算法尤其具有挑战性，就像今天的网络中经常出现的情况一样。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散到数据源。一种更灵活的方法，即智能地将数据带到学习算法的计算组件，并反过来将计算带到数据源，可以更好地利用这些设备的资源，但也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，启用新型分布式学习算法。该项目的成果可能会在从智能城市到卫星数据分析到增强现实的大量潜在应用中改善分布式学习。该项目还支持正在进行的教育努力，并将计算机的参与扩大到代表性不足的社区。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)招募高中生和本科生从事有助于研究愿景的适当范围的项目，以及(Iii)旨在增加未被充分代表的少数群体参与计算的演讲和辅导会议。该项目是卡内基梅隆大学和东北大学的合作努力。结果，包括算法实施、技术报告和测量数据集，将在CMU托管的存储库中公开提供。这些奖项将在项目结束后至少两年内保留。这一奖项反映了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。

项目成果

Can we Generalize and Distribute Private Representation Learning?

• 发表时间: 2020-10
• 作者: Sheikh Shams Azam;Taejin Kim;Seyyedali Hosseinalipour;Carlee Joe-Wong;S. Bagchi;Christopher G. Brinton

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

• DOI: 10.1609/aaai.v36i7.20785
• 发表时间: 2021-12
• 作者: Yichen Ruan;Carlee Joe-Wong

AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning

• DOI: 10.1109/iwqos57198.2023.10188807
• 发表时间: 2023-06
• 期刊: 2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)
• 作者: Weijie Liu-;Xiaoxi Zhang;Jingpu Duan;Carlee Joe-Wong;Zhi Zhou;Xu Chen

GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing

• DOI: 10.48550/arxiv.2305.14562
• 发表时间: 2023-05
• 期刊: ArXiv
• 作者: Yi Hu;Chao Zhang;E. Andert;Harshul Singh;Aviral Shrivastava;J. Laudon;Yan-Quan Zhou;Bob Iannucci;Carlee Joe-Wong

EdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning

• DOI: 10.1109/secon58729.2023.10287414
• 发表时间: 2023-09
• 期刊: 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
• 作者: Shaohui Lin;Xiaoxi Zhang;Yupeng Li;Carlee Joe-Wong;Jingpu Duan;Xu Chen