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

基本信息

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

项目摘要

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，已经以分布式的方式从物联网设备（例如智能传感器，智能手机，相机等）以分布式方式生成了大量数据，以有效地学习分布式数据，分布式学习/联合学习框架之间的模式，以有效地探索了分布式的分布式/宾语数据资源。但是，当前关于物联网的分布式学习系统面临不可避免的挑战，主要归类为安全，隐私，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处于相同的类型/维度。第三，现有框架的学习效率是另一个主要问题。这项工作旨在在物联网设备上系统地设计一种安全且完全分散的学习方法，使系统能够从异源数据（即不同类型/维度中的数据）中学习模式。这项工作还将在分散的平台，即区块链和设计相应的系统协议上部署开发的方法，以消除像传统分布式学习一样使用中央服务器的需求。另一方面，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，并理论上分析系统的可靠性。此外，这项工作为分散学习支持的密集物联网应用程序中的系统研究奠定了基础。它产生了制定独立和竞争性研究议程所需的初步实验数据。这项工作将将传统的分布式学习/联合学习扩展到物联网下的更一般和实际的场景中，在该方案中，系统中存在各种类型的数据和物联网设备。当网络中的一定部分受到攻击/恶意，这项工作还将提高学习绩效的可靠性。此外，这项工作具有潜在的变革性，因为它可能有助于为众多应用程序（例如智能城市，智能家居和移动健康）生成创新且安全的分散深度学习技术，因为拟议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或分布式智能应用程序转换为具有安全性和性能保证的完全分散的方式，对研究产生重大影响。为了解决教育和社会方面，该项目旨在为学生提供资源，以追求STEM的高级学位和职业。这项工作计划使来自代表性不足的小组的学生成为不断努力的一部分，以扩大计算和开发高级研究生级课程，以在未来对IOT的人工智能分散的人工智能中引入新的趋势和网络安全挑战。该奖项颁发了NSF的法定任务，并反映了通过评估的支持者的支持者，该奖项已被评估范围众所周知。