EAGER: CoFedAI: Cost-sensitive Federated AI for Smart Manufacturing Data-Sharing

这个探索性研究（EAGER）项目将研究一种成本敏感的数据共享范式，该范式集成了来自多个制造商的制造数据，以改善智能制造中的监督学习。这一努力解决了在美国制造业中实施人工智能（AI）的一个关键问题，因为人工智能方法受益于大型数据集的训练，而制造商通常会对其数据保密。研究人员将研究保护数据隐私的方法，目标是使未来的制造服务基础设施能够聚合、管理和重用来自多个制造商的数据。这样的基础设施可以通过建立数据共享市场使制造业受益，从而实现国内合作伙伴关系并加速人工智能技术的采用，从而提高美国的国际市场份额。这项工作的各个方面也将被纳入pi教授的课程中。该项目通过创建特定于任务的相似性指标和区分来自多个制造数据所有者的贡献的方法，为制造数据共享生态系统奠定了基础。选择合适的数据源进行数据聚合取决于对来自不同数据源的数据在数据分布和变量关系上的相似性进行分类。成本敏感的联邦人工智能（CoFedAI）框架将促进数据交换，通过采用成本敏感的多武装数据共享框架，向多个利益相关者请求数据，从而释放制造业人工智能知识转移的价值。分层框架将多臂强盗扩展到多个数据源，并将相似度分解为两个相互关联的元素：制造商相似度和数据相似度。此外，该框架将基于相似性度量评估和区分制造业中来自多个数据所有者的贡献。在后续工作中，pi将扩展预想的生态系统，以促进自然语言查询、制造商约束的集成和新颖的数据共享激励。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。

This EArly-concept Grant for Exploratory Research (EAGER) project will investigate a cost-sensitive data-sharing paradigm that integrates manufacturing data from multiple manufacturers to improve supervised learning in smart manufacturing. This effort addresses a critical problem in the implementation of Artificial Intelligence (AI) in US manufacturing, since AI methods benefit from training on large datasets and manufacturers typically keep their data secret. The investigators will research methods for preserving the privacy of that data, with the goal of enabling a future manufacturing service infrastructure to aggregate, manage and reuse data from multiple manufacturers. Such an infrastructure can benefit manufacturing by establishing a data-sharing marketplace that enables domestic partnerships and accelerates the adoption of AI technologies, thus enhancing the international market share of the United States. Aspects of this work will also be incorporated into the courses taught by the PIs.The project lays the foundation for a manufacturing data-sharing ecosystem by creating task-specific similarity metrics and a methodology to differentiate the contributions from multiple manufacturing data owners. The selection of suitable data sources for data aggregation depends on the categorization of the data derived from the various sources for similarity in data distribution and variable relationship. The Cost-sensitive Federated AI (CoFedAI) framework will facilitate data exchange to unlock the value of knowledge transfer for AI in manufacturing by employing a cost-sensitive multi-armed bandit data-sharing framework that requests data from multiple stakeholders. The hierarchical framework will extend the multi-armed bandit to multiple data sources and decompose similarity into two interconnected elements: manufacturer similarity and data similarity. In addition, the framework will assess and differentiate the contributions from multiple data owners in manufacturing based on the similarity metrics. In subsequent work, the PIs will extend the envisioned ecosystem to facilitate natural language queries, integration of manufacturer constraints, and novel data-sharing incentives.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.