CRII: CNS: Secure Decentralized AI in Heterogeneous IoT Networks: Foundation and Application

CRII：CNS：异构物联网网络中的安全去中心化人工智能：基础与应用

• 批准号: 2245933
• 负责人: Qianlong Wang
• 金额: $ 17.42万
• 依托单位: Towson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245933&HistoricalAwards=false
• 关键词: CRII; CNS; Secure; Decentralized; AI

Under the emergence of the Internet of Things (IoT), tremendous amounts of data have been generated in a distributed manner from IoT devices, e.g., smart sensors, smartphones, cameras, etc. To effectively learn the patterns among such distributed data, distributed learning/federated learning frameworks have been explored to effectively utilize the distributed/decentralized data resource. However, the current distributed learning systems over IoT face inevitable challenges, mainly categorized into security, privacy, compatibility, and efficiency. For instance, first, the success of most existing learning systems relies heavily on a central server to coordinate the learning process. Second, traditional distributed learning requires the data on different devices to be in the same type/dimension. Third, the learning efficiency of the existing frameworks is another major concern. This work aims to systematically design a secure and fully decentralized learning method over IoT devices that enables the system to learn patterns from heterogenous data, i.e., data in different types/dimensions. This work will also deploy the developed method on a decentralized platform, i.e., blockchain, and design corresponding system protocols to eliminate the need to use a central server as in traditional distributed learning. On the other hand, the proposed system aims to exploit potential system vulnerabilities, develop attack and defense mechanisms, and theoretically analyze the system's reliability. Moreover, this work lays the groundwork for system research in dense IoT applications supported by decentralized learning. It generates preliminary experimental data necessary to develop an independent and competitive research agenda. This work will extend the traditional distributed/federated learning into a more general and practical scenario under IoT, where various types of data and IoT devices exist in the system. This work will also enhance the reliability of learning performance when a certain portion of the data in the network is attacked/malicious. Additionally, this work is potentially transformative as it may help generate innovative and secure decentralized deep learning techniques for numerous applications, e.g., smart cities, smart homes, and mobile health, since the proposed system can effectively utilize heterogeneous resources. It could also have significant impacts on research in transforming the existing centralized or distributed smart applications into a fully decentralized manner with security and performance guarantees. To address the education and social aspects, this project aims to provide students with resources to pursue advanced degrees and careers in STEM. This work plans to engage students from underrepresented groups as a part of a continuous effort to broaden participation in computing and develop advanced graduate-level courses to introduce new trends and cybersecurity challenges in future decentralized artificial intelligence over IoT.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.

在物联网（IoT）的出现下，智能传感器、智能手机、相机等物联网设备以分布式方式产生了大量数据。为了有效地学习这些分布式数据之间的模式，人们探索了分布式学习/联邦学习框架，以有效地利用分布式/去中心化的数据资源。然而，当前基于物联网的分布式学习系统面临不可避免的挑战，主要分为安全性、隐私性、兼容性和效率。例如，首先，大多数现有学习系统的成功在很大程度上依赖于中央服务器来协调学习过程。其次，传统的分布式学习要求不同设备上的数据具有相同的类型/维度。第三，现有框架的学习效率是另一个主要问题。这项工作旨在系统地设计一种基于物联网设备的安全且完全去中心化的学习方法，使系统能够从异构数据（即不同类型/维度的数据）中学习模式。这项工作还将把开发的方法部署在去中心化平台，即区块链上，并设计相应的系统协议，以消除传统分布式学习中使用中央服务器的需要。另一方面，所提出的系统旨在利用潜在的系统漏洞，开发攻击和防御机制，并对系统的可靠性进行理论分析。此外，这项工作为去中心化学习支持的密集物联网应用的系统研究奠定了基础。它生成制定独立且有竞争力的研究议程所需的初步实验数据。这项工作将传统的分布式/联邦学习扩展到物联网下更通用和实用的场景，其中系统中存在各种类型的数据和物联网设备。当网络中的某一部分数据受到攻击/恶意攻击时，这项工作还将增强学习性能的可靠性。此外，这项工作具有潜在的变革性，因为它可能有助于为智能城市、智能家居和移动健康等众多应用产生创新且安全的去中心化深度学习技术，因为所提出的系统可以有效地利用异构资源。它还可能对将现有的集中式或分布式智能应用程序转变为具有安全性和性能保证的完全去中心化方式的研究产生重大影响。为了解决教育和社会方面的问题，该项目旨在为学生提供获得 STEM 高级学位和职业的资源。这项工作计划让来自代表性不足群体的学生参与进来，作为持续努力的一部分，以扩大对计算的参与并开发高级研究生水平课程，以介绍未来物联网去中心化人工智能的新趋势和网络安全挑战。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标准进行评估，被认为值得支持。

项目成果

Blockchain-Empowered Federated Learning Through Model and Feature Calibration

• DOI: 10.1109/jiot.2023.3311967
• 发表时间: 2024-02
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Qianlong Wang;Weixian Liao;Y. Guo;Michael McGuire;Wei Yu