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各种冲击和灾难，包括Covid-19。这可以提高网络弹性，透明度和网络安全已成为国家优先事项。诸如人工智能和机器学习之类的智能制造技术在实现这些目标方面表现出了希望，但很难在制造网络水平上实现。特别是中小型制造商在采用这些数据驱动的增值技术方面挣扎，因为资源和激励措施不足。因此，他们不能参与许多通常需要某些技术和数据共享的高价值制造网络。这项对探索性研究（急切）项目的早期概念赠款支持旨在通过支持区块链的框架来应对这一挑战的研究，该框架利用安全和私人联​​盟的学习来满足国防制造网络的独特要求。该框架提高了关键供应的可用性和完整性，并提高了优势，并使国防工业基础多样化。该项目的安全和隐私的数据共享和协作机制可以应用于制造业以外的各个领域，例如医疗保健，金融和供应链，赋予个人和组织的能力，以安全地共享数据并有效地共享数据。结果有可能改变行业，推动经济增长，促进创新并增强社会福祉。该项目的研究问题工厂从制造网络的无法安全地交换数据并利用联合学习的网络级别。该项目旨在提高分布式和动态制造网络的弹性，特别是包括中小型制造商，通过提供对安全的私人区块链平台的访问，该平台可实现分散，安全和透明的通信渠道。这使制造网络级别通过联合学习，同时尊重数据所有权，并确保保留竞争性或受控（原始）数据和机器学习模型。为了实现这些目标，该项目通过集成私人区块链来管理元数据，访问控件和模型更新来利用联合学习。与现有方法不同，该框架着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持本地状态，同时利用区块链有效地协调联合学习过程，并降低资源约束的较小网络参与者的间接费用。该项目通过有效的算法来推进联合学习和区块链技术在制造网络的异质数据的存在下，通过有效的模型聚合和协调。该奖项反映了NSF的法定任务，并被认为是通过使用该基金会的智力和更广泛影响的评估来评估的支持，并被视为珍贵的支持。