EAGER: Private Blockchain-Enabled Federated Learning Framework for Distributed Manufacturing Networks

EAGER：支持私有区块链的分布式制造网络联合学习框架

• 批准号: 2420964
• 负责人: Thorsten Wuest
• 金额: $ 30万
• 依托单位: West Virginia University Research Corporation
• 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: 2024-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2420964&HistoricalAwards=false
• 关键词: EAGER; Private; Blockchain; Enabled; Federated

In recent years, global manufacturing networks experienced a variety of shocks and disturbances including COVID-19. Thus, improving network resiliency, transparency, and cybersecurity have emerged as a national priority. Smart Manufacturing technologies such as Artificial Intelligence and Machine Learning show promise in achieving these objectives, yet struggle to materialize at the manufacturing network level. Particularly small and medium-sized manufacturers struggle in their adoption of these data-driven, value added technologies due to a lack of resources and incentives. Consequently, they cannot participate in many high-value manufacturing networks that often require certain technologies and data sharing. This EArly-concept Grant for Exploratory Research (EAGER) project supports research that intends to address this challenge through a Blockchain-enabled framework that leverages secure and private Federated Learning which meets the unique requirements of defense manufacturing networks. This framework enhances the availability and integrity of critical supplies, as well as strengthens and diversifies the defense industrial base. The project’s secure and privacy-preserving data sharing and collaboration mechanisms can be applied in various domains beyond manufacturing, such as healthcare, finance, and supply chain, empowering individuals and organizations to share data securely and collaborate effectively. The results have potential to transform industry, drive economic growth, foster innovation, and enhance societal well-being. The project’s research problem stems from manufacturing networks’ inability to securely and efficiently exchange data and leverage network level Federated Learning. The project aims to increase the resiliency of distributed and dynamic manufacturing networks, specifically including small and medium-sized manufacturers, by providing access to a secure private Blockchain platform that enables decentralized, secure, and transparent communication channels. This enables manufacturing network level learning through Federated Learning while respecting data ownership and ensuring retention of competitive or controlled (raw) data and machine learning models. To achieve these goals, the project utilizes Federated Learning by integrating a private Blockchain to manage metadata, access controls, and model updates. Unlike existing approaches, the framework focuses on specific challenges and requirements of manufacturing networks. This means ensuring confidential data remains local under full control of the individual nodes while leveraging Blockchain for efficient coordination of the Federated Learning process as well as reducing overhead cost for smaller network participants that are resource constraint. The project advances the state-of-the-art in Federated Learning and Blockchain technology through efficient algorithms for model aggregation and coordination in the presence of heterogeneous data for manufacturing networks.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.

近年来，全球制造网络经历了包括 COVID-19 在内的各种冲击和干扰，因此，提高网络弹性、透明度和网络安全已成为国家优先事项，例如人工智能和机器学习等智能制造技术有望实现这一目标。这些目标，但在制造网络层面难以实现，特别是中小型制造商由于缺乏资源和激励措施而难以采用这些数据驱动的增值技术，他们无法参与许多高水平的项目。 -经常重视制造网络这个早期概念探索性研究资助 (EAGER) 项目支持旨在通过区块链支持的框架应对这一挑战的研究，该框架利用安全和私有的联合学习来满足国防制造网络的独特要求。该框架增强了关键供应品的可用性和完整性，并增强了国防工业基础并使其多样化。该项目的安全和隐私保护数据共享和协作机制可应用于制造以外的各个领域，例如医疗保健、该项目的研究问题源于制造网络的无能。安全有效地交换数据并利用网络级联合学习该项目旨在通过提供对安全私有区块链平台的访问来提高分布式动态制造网络（特别是中小型制造商）的弹性，该平台可实现分散、安全。 ，并且透明这使得能够通过联邦学习进行制造网络级学习，同时尊重数据所有权并确保保留竞争或受控（原始）数据和机器学习模型。为了实现这些目标，该项目通过集成私有区块链来管理元数据和访问控制，从而利用联邦学习。与现有方法不同，该框架侧重于制造网络的特定挑战和要求，这意味着确保机密数据在各个节点的完全控制下保留在本地，同时利用区块链有效协调联邦学习过程并减少数据丢失。间接费用该项目通过在制造网络存在异构数据的情况下进行模型聚合和协调的有效算法，推动了联邦学习和区块链技术的发展。该奖项反映了 NSF 的法定使命和使命。通过使用基金会的智力价值和更广泛的影响审查标准进行评估，该项目被认为值得支持。