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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。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IoT范式从“互联事物”转变为“互联智能”。鉴于机密物联网数据的存在和数据泄露的广泛影响，联合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密物联网数据来实现知识共享。然而，经典的联合学习假设同质参与设备具有丰富的标签数据，这与大多数物联网设备的属性不一致，例如资源限制、异构性和缺乏注释。为了在物联网系统中释放联合设备智能的潜力，本项目关注两个关键但尚未解决的问题：(I)以无数据的方式跨资源受限的异构物联网设备进行联合知识共享；(Ii)在不断变化的物联网环境下，使用有限的地面真实标签知识进行联合领域适配。该项目开发了新颖和实用的方法，以解决在知识生成和转移阶段在准确性、效率和数据依赖方面相互冲突的目标。建议的研究将在模拟器驱动和真实世界测试台上进行全面和严格的评估。该项目将在实际但具有挑战性的物联网环境中推进对联合学习的当前理解，应对广泛参与联合设备智能的挑战，并支持具有联合智能的引人注目的新物联网应用。该项目的研究成果将与现有课程和K-12课程整合，并通过会议、研讨会和出版物传播，以加快人工智能和物联网研究的进展。此外，该项目将积极吸纳本科生和代表性不足的学生，并改善计算机科学和工程研究中代表性不足的少数群体的存在。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.