RII Track-4:NSF: HEAL: Heterogeneity-aware Efficient and Adaptive Learning at Clusters and Edges

RII Track-4：NSF：HEAL：集群和边缘的异质性感知高效自适应学习

• 批准号: 2327452
• 负责人: Li Chen
• 金额: $ 28.24万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327452&HistoricalAwards=false
• 关键词: RII; Track; NSF; HEAL; Heterogeneity

With the proliferation of the Internet of Things (IoT) and technological advances in deep learning, various application domains have witnessed the growing adoption of Artificial Intelligence (AI), such as augmented reality, autonomous driving, and smart healthcare. Effectively learning from the ever-expanding pool of data generated by IoT devices poses a unique challenge due to data regulations and privacy concerns. Federated learning shows promise as a method for collaboratively training models on edge devices without exposing sensitive data. However, deploying federated learning in real-world IoT networks remains challenging due to the heterogeneity of systems and data, as well as the coexistence of multiple jobs. This project aims to address such challenges with a systematic solution, HEAL, for Heterogeneity-aware Efficient and Adaptive Learning for multiple jobs in a shared IoT network. The fellowship will provide support for the PI and her graduate student to conduct essential experimental investigations at the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign, leveraging advanced cyberinfrastructure, cutting-edge technologies, diverse datasets, and abundant domain expertise at this interdisciplinary research institute. The project outcome will advance the knowledge and understanding of collaborative learning in the edge-cloud continuum and provide guidance for AI-driven applications on shared IoT infrastructure.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant professor and training for a graduate student at the University of Louisiana at Lafayette. This work would be conducted in collaboration with researchers at the University of Illinois Urbana-Champaign. This project aims to address the unique challenges encountered when implementing practical federated learning in real-world IoT networks, catering to the growing diversity of machine learning applications. A systematic solution will be designed for Heterogeneity-aware Efficient and Adaptive Learning (HEAL) for multiple jobs in a shared IoT network, synergizing two major thrusts: adaptive offloading of on-device training computation to the edge server and judicious selection and scheduling of participant devices for concurrent learning jobs. It will systematically and experimentally investigate a number of knotty issues in multi-job federated learning in a shared heterogeneous IoT infrastructure. The major components with novelty are: (1) The adaptive offloading of training computation from heterogeneous edge devices that can strike a balance between computation, communication, and privacy leakage risk; and (2) The judicious coordination of edge devices in the distributed training procedures of multiple concurrent learning jobs, aiming for system efficiency and model quality. The proposed solution will be deployed and tested in real-world IoT networks at the host site over diverse learning applications, not only providing solutions at the algorithmic level but also producing practical implications and insights. The anticipated project outcomes will enrich educational materials and strengthen curriculum development in the areas of machine learning systems, distributed systems and networking, cloud and edge computing, and resource scheduling. This project will enable the PI to establish a long-term collaboration with national prominence and enhance the research capacity of her home institution.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.

随着物联网（IoT）的普及和深度学习技术的进步，人工智能（AI）的应用日益广泛，例如增强现实、自动驾驶和智能医疗等。由于数据法规和隐私问题，有效地从物联网设备生成的不断扩大的数据池中学习提出了独特的挑战。联邦学习有望作为一种在边缘设备上协作训练模型而不暴露敏感数据的方法。然而，由于系统和数据的异构性以及多种作业的共存，在现实世界的物联网网络中部署联邦学习仍然具有挑战性。该项目旨在通过系统解决方案 HEAL 来应对这些挑战，以实现共享物联网网络中多项工作的异质性感知高效和自适应学习。该奖学金将为 PI 和她的研究生提供支持，利用先进的网络基础设施、尖端技术、多样化的数据集和丰富的跨学科领域专业知识，在伊利诺伊大学厄巴纳-香槟分校的协调科学实验室进行重要的实验研究研究所。该项目成果将增进对边缘-云连续体中协作学习的认识和理解，并为共享物联网基础设施上的人工智能驱动应用提供指导。该研究基础设施改进 Track-4 EPSCoR 研究人员 (RII Track-4) 项目将提供路易斯安那大学拉斐特分校的助理教授奖学金和研究生培训。这项工作将与伊利诺伊大学厄巴纳-香槟分校的研究人员合作进行。该项目旨在解决在现实世界物联网网络中实施实用联邦学习时遇到的独特挑战，满足机器学习应用日益多样化的需求。将为共享物联网网络中的多个作业设计一个系统的解决方案，用于异构感知的高效自适应学习（HEAL），协同两个主要目标：将设备上的训练计算自适应卸载到边缘服务器，以及明智地选择和调度参与者用于并发学习作业的设备。它将系统地和实验性地研究共享异构物联网基础设施中多作业联合学习中的许多棘手问题。新颖的主要组成部分是：（1）从异构边缘设备自适应卸载训练计算，可以在计算、通信和隐私泄露风险之间取得平衡； （2）在多个并发学习作业的分布式训练过程中明智地协调边缘设备，以提高系统效率和模型质量。所提出的解决方案将在主机站点的真实物联网网络中通过各种学习应用程序进行部署和测试，不仅提供算法级别的解决方案，而且还产生实际影响和见解。预期的项目成果将丰富教育材料并加强机器学习系统、分布式系统和网络、云和边缘计算以及资源调度等领域的课程开发。该项目将使 PI 能够与国家知名机构建立长期合作，并提高其所在机构的研究能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。