CRII: CNS: IoT-aware Federated On-Device Intelligence

CRII：CNS：物联网感知的联合设备上智能

• 批准号: 2418308
• 负责人: Lan Zhang
• 金额: $ 17.5万
• 依托单位: Clemson University
• 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: 2024-01-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2418308&HistoricalAwards=false
• 关键词: CRII; CNS; IoT; aware; Federated

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Recent breakthroughs in on-device machine learning bring artificial intelligence (AI) closer to Internet-of-Things (IoT) devices, shifting the IoT paradigm from “connected things” to “connected intelligence.” Given the presence of confidential IoT data and the widespread impact of data breaches, federated learning provides a privacy-preserving solution that enables knowledge sharing by exchanging on-device model updates rather than private IoT data. However, classical federated learning assumes homogeneous participating devices with a wealth of labeled data, which stands at odds with the properties of most IoT devices, such as resource constraints, heterogeneity, and lack of annotation. To unleash the potential of federated on-device intelligence in IoT systems, this project focuses on two critical yet open problems: (i) federated knowledge sharing across resource-constrained and heterogeneous IoT devices in a data-free manner; and (ii) federated domain adaptation with limited ground truth labeled knowledge under ever-changing IoT environments. The project develops novel and practical approaches to address conflicting goals on accuracy, efficiency, and data dependence at both the knowledge generation and transfer stages. The proposed research will be thoroughly and rigorously evaluated in simulator-driven and real-world testbeds.This project will advance the current understanding of federated learning in practical yet challenging IoT environments, address challenges to broad participation of federated on-device intelligence, and enable compelling new IoT applications with federated intelligence. The research outcomes of this project will be integrated with existing curriculums and K-12 programs and disseminated through conferences, seminars, and publications to accelerate progress in AI and IoT research. Furthermore, this project will actively involve undergraduate and underrepresented students and improve the presence of underrepresented minorities in computer science and engineering research.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.

该奖项全部或部分由2021年美国救援计划法案（Public Law 117-2）资助。设备上机器学习的最新突破使人工智能（AI）更接近物联网（IoT）设备，将物联网范式从“连接的事物”转变为“连接的智能”。鉴于存在机密物联网数据以及数据泄露的广泛影响，联邦学习提供了一种隐私保护解决方案，通过交换设备上的模型更新而不是私有物联网数据来实现知识共享。然而，经典的联邦学习假设具有大量标记数据的同质参与设备，这与大多数物联网设备的属性不一致，例如资源约束，异构性和缺乏注释。为了释放物联网系统中联合设备智能的潜力，该项目重点关注两个关键但尚未解决的问题：（i）以无数据的方式在资源受限和异构物联网设备之间共享联合知识;以及（ii）在不断变化的物联网环境下，联合域自适应有限的地面真值标记知识。该项目开发了新颖实用的方法，以解决在知识生成和转移阶段的准确性，效率和数据依赖性方面相互冲突的目标。该项目将在模拟器驱动和真实世界的测试平台上进行全面而严格的评估。该项目将推进当前对实际但具有挑战性的物联网环境中联邦学习的理解，解决联邦设备智能广泛参与的挑战，并通过联邦智能实现引人注目的新物联网应用。该项目的研究成果将与现有的学术研讨会和K-12计划相结合，并通过会议、研讨会和出版物传播，以加速人工智能和物联网研究的进展。此外，该项目将积极参与本科生和代表性不足的学生，并改善代表性不足的少数民族在计算机科学和工程研究中的存在。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。