CRII: CSR: Adaptive Federated Continuous Learning on Heterogeneous Edge Devices with Unlabeled Data

CRII：CSR：具有未标记数据的异构边缘设备的自适应联合连续学习

• 批准号: 2348279
• 负责人: Letian Zhang
• 金额: $ 17.48万
• 依托单位: Middle Tennessee State 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: 2024-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348279&HistoricalAwards=false
• 关键词: CRII; CSR; Adaptive; Federated; Continuous

With the growing success of artificial intelligence (AI) techniques, especially deep neural networks (DNNs), there has been an ongoing push to introduce AI services across different domains, including healthcare, autonomous driving, image processing, and more. Due to data privacy issues and unlabeled client datasets, AI service providers must often collect and label their own datasets, and then train their models offline prior to deployment. However, these pre-trained DNNs may not capture new patterns of online data; they must typically be retrained on user-supplied data. This introduces several challenges: (1) Data Privacy: Users are increasingly concerned about unauthorized access to their private data. (2) Unlabeled Heterogeneous Data: Edge devices are typically deployed in diverse environments and owned by a range of users, leading to substantial variation in the distribution of local data. Users may also lack the motivation and/or expertise to adequately label their data. (3) Device Heterogeneity: Edge devices exhibit a wide spectrum of computing and memory capabilities, and retraining DNN models on such heterogeneous edge devices can be time-consuming. To overcome these challenges, this project proposes an adaptive, federated, continuous learning system, which uses a novel federated, semi-supervised learning framework to retrain DNN models on distributed, unlabeled, heterogeneous data, while leveraging explainable AI techniques to expedite local training. This project holds promise for improving AI service adaptation in real-world scenarios by bolstering privacy, adaptability, and efficiency. It supports seamless integration of AI services across diverse domains, while ensuring data privacy and optimizing model performance on edge/client devices. This project also contains a significant educational component. It will provide opportunities to involve students from groups underrepresented in computing, fostering diversity and inclusion. This will have a positive impact on these students’ education and careers.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.

随着人工智能（AI）技术的越来越多的成功，尤其是深神经网络（DNNS），一直在不断推动跨不同领域引入AI服务，包括医疗保健，自动驾驶，图像处理等。由于数据隐私问题和未标记的客户端数据集，AI服务提供商必须经常收集和标记自己的数据集，然后在部署前离线训练模型。但是，这些预训练的DNN可能无法捕获在线数据的新模式。它们通常必须在用户提供的数据上重新训练。这引入了一些挑战：（1）数据隐私：用户越来越关注未经授权访问其私人数据。 （2）未标记的异构数据：边缘设备通常部署在潜水员环境中，并由一系列用户所有，从而导致本地数据分布的实质性差异。用户还可能缺乏充分标记其数据的动力和/或专业知识。 （3）设备异质性：边缘设备具有广泛的计算和内存功能，并且在此类异质边缘设备上的重新培训DNN模型可能很耗时。为了克服这些挑战，该项目提出了一种自适应，联合，持续的学习系统，该系统使用一种新颖的联盟，半监督的学习框架来重新研究DNN模型，以分布式，未标记的，异构的数据，同时利用可解释的AI AI技术来进行本地培训。该项目有望通过增强隐私，适应性和效率来改善现实情况下的AI服务适应。它支持跨潜水员域的AI服务的无缝集成，同时确保数据隐私并在边缘/客户端设备上优化模型性能。该项目还包含重要的教育组成部分。它将提供机会，让来自人数不足的学生参与计算，促进多样性和包容性。这将对这些学生的教育和职业产生积极影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是宝贵的支持。