Collaborative Research: CNS Core: Small: Edge AI with Streaming Data: Algorithmic Foundations for Online Learning and Control

合作研究：中枢神经系统核心：小型：具有流数据的边缘人工智能：在线学习和控制的算法基础

• 批准号: 2225949
• 负责人: Lei Jiao
• 金额: $ 29.99万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225949&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Many emerging applications, such as smart healthcare, autonomous driving, and augmented reality, rely on applying real-time Artificial Intelligence (AI) to streaming data that are constantly generated online. Edge AI, which moves AI services to the network edge close to the end users and devices where data streams are generated, is crucial for reducing latency and communication bottlenecks and enabling fast and accurate inference decisions. However, edge AI for online streaming data poses significant challenges due to the unpredictable dynamics of the streaming data and the limited computation/communication capability at the network edge. This project addresses these challenges by developing both new theoretic models that integrate sophisticated learning methods with advanced edge-network control, and practical algorithms that significantly improve the accuracy and timeliness of edge AI services for streaming data. Specifically, the project will focus on three closely-related thrusts: (i) online learning policies for model selection will be developed to quickly identify which machine-learning models should be dynamically deployed at the edge servers for best inference accuracy, while accounting for the heterogeneous switching and feedback costs; (ii) distributed online transfer learning methods will be developed to quickly retrain new machine learning models at the edge upon new streaming data; and (iii) partial-index based edge-network control policies will be developed to optimize the timeliness of interactive edge-AI services under tight resource constraints.Both edge networks and AI are considered crucial elements of next-generation wireless networks. This project will directly benefit network operators and service providers that deploy and operate edge-AI systems. Specifically, the results will help them automate the complex decision-making process required for the end-to-end orchestration of such systems, and improve the accuracy and timeliness of the edge-AI services despite the constantly-changing environments. This project will also benefit the end users of emerging applications powered by edge AI, improving their user experience and well-being. More broadly, the theories and algorithms developed in this project for learning/control co-design will not only transform edge AI, but also benefit other disciplines with similar requirements for optimization under significant dynamism and uncertainty. Finally, this project will contribute teaching and training materials to multiple undergraduate and graduate courses, and will engage women and underrepresented minority students by reaching out to local schools.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.

许多新兴应用，例如智能医疗、自动驾驶和增强现实，都依赖于将实时人工智能 (AI) 应用于不断在线生成的流数据。边缘人工智能将人工智能服务移动到靠近最终用户和生成数据流的设备的网络边缘，对于减少延迟和通信瓶颈以及实现快速准确的推理决策至关重要。然而，由于流数据动态的不可预测性以及网络边缘有限的计算/通信能力，在线流数据的边缘人工智能提出了重大挑战。该项目通过开发将复杂的学习方法与先进的边缘网络控制相结合的新理论模型和可显着提高流数据边缘人工智能服务的准确性和及时性的实用算法来应对这些挑战。具体来说，该项目将重点关注三个密切相关的主旨：（i）将开发用于模型选择的在线学习策略，以快速确定哪些机器学习模型应动态部署在边缘服务器上，以获得最佳推理精度，同时考虑到异构切换和反馈成本； (ii) 将开发分布式在线迁移学习方法，以在新的流数据边缘快速重新训练新的机器学习模型； (iii) 将制定基于部分索引的边缘网络控制策略，以在资源紧张的情况下优化交互式边缘人工智能服务的及时性。边缘网络和人工智能都被认为是下一代无线网络的关键要素。该项目将直接使部署和运营边缘人工智能系统的网络运营商和服务提供商受益。具体来说，研究结果将帮助他们实现此类系统端到端编排所需的复杂决策过程的自动化，并在环境不断变化的情况下提高边缘人工智能服务的准确性和及时性。该项目还将让边缘人工智能驱动的新兴应用程序的最终用户受益，改善他们的用户体验和福祉。更广泛地说，该项目中开发的用于学习/控制协同设计的理论和算法不仅将改变边缘人工智能，而且还将有利于在巨大的动态和不确定性下具有类似优化要求的其他学科。最后，该项目将为多个本科生和研究生课程提供教学和培训材料，并将通过接触当地学校来吸引女性和代表性不足的少数族裔学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

Scheduling In-Band Network Telemetry With Convergence-Preserving Federated Learning

通过保持收敛的联邦学习来调度带内网络遥测

• DOI: 10.1109/tnet.2023.3253302
• 发表时间: 2023-03
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Jin, Yibo;Jiao, Lei;Ji, Mingtao;Qian, Zhuzhong;Zhang, Sheng;Chen, Ning;Lu, Sanglu
• 通讯作者: Lu, Sanglu

EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines

EAVS：具有细粒度无服务器管道的边缘辅助自适应视频流

• 发表时间: 2023-01
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Hou, Biao;Yang, Song;Kuipers, Fernando A.;Jiao, Lei;Fu, Xiaoming
• 通讯作者: Fu, Xiaoming

Online training data acquisition for federated learning in cloud-edge networks

云边缘网络联邦学习的在线训练数据获取

• 发表时间: 2023-01
• 期刊: Computer networks
• 影响因子: 5.6
• 作者: Zhu, Konglin;Chen, Wentao;Jiao, Lei;Wang, Jiaxing;Peng, Yuyang;Zhang, Lin
• 通讯作者: Zhang, Lin

Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks

在边缘网络中通过去中心化联合学习来协调区块链

• 发表时间: 2023-01
• 期刊: and Networking (SECON
• 作者: Jin, Yibo;Jiao, Lei;Qian, Zhuzhong;Zhou, Ruiting;Pu, Lingjun
• 通讯作者: Pu, Lingjun

Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions

迈向可持续人工智能：通过拍卖实现云边缘系统中的联邦学习需求响应

• 发表时间: 2023-01
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Wang, Fei;Jiao, Lei;Zhu, Konglin;Lin, Xiaojun;Li, Lei
• 通讯作者: Li, Lei