CCSS: Distributed Swarm Learning for Internet of Things at the Edge

CCSS：边缘物联网的分布式群体学习

• 批准号: 2231209
• 负责人: Zhi Tian
• 金额: $ 40万
• 依托单位: George Mason 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: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231209&HistoricalAwards=false
• 关键词: CCSS; Distributed; Swarm; Learning; Internet

With the vigorous growth of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless IoT networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm. Because IoT networks have massive low-cost edge devices with limited capabilities and resources, IoT-driven edge learning faces major technical challenges caused by the communication bottleneck, data and device heterogeneity, non-convex optimization, privacy and security concerns, and dynamic operating environments. To overcome these challenges, this project builds a new framework of distributed swarm learning (DSL) through a holistic integration of artificial intelligence and biological swarm intelligence. The proposed DSL framework for edge learning is expected to benefit a wide range of IoT applications such as autonomous fleet management, massive wearable electronics, smart agriculture, to name a few. This project also provides broader societal impacts through student training, workforce development, research dissemination and outreach to minorities and local communities.This objective of this project is to develop an efficient distributed learning framework that coherently addresses the unique technical challenges related to swarm IoT with device restrictions and resource constraints on communication, computation and data, and in complicated edge environments with potential link failure, attacks, and topology changes. First, a new DSL framework is established by bridging federated learning with swarm optimization techniques. With theoretical backing, efficient information extraction and exchanging mechanisms are developed along with parsimonious transmission schemes for high efficiency in both communication and computation of model updates. Second, to cope with data heterogeneity, link failure and malicious attacks in practical IoT systems, robust DSL techniques are developed based on generative adversarial networks, multi-worker selection and analog transmission-and-aggregation techniques. Finally, to handle streaming data in an online fashion at the network edge, dynamic optimization techniques are investigated via the design of adaptive weights and exploration-exploitation strategies under the DSL framework. The outcomes of this research are expected to contribute to novel tools for learning and optimization tailored to real-time operation of large-scale IoT in dynamic environments, with technological impacts on statistical learning, signal processing and wireless communications.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.

随着多功能物联网（IoT）服务的蓬勃发展，智能物联网设备越来越多地部署在无线物联网网络边缘，利用本地收集的数据执行协作机器学习任务，从而催生了边缘学习范式。由于物联网网络拥有大量低成本边缘设备，但能力和资源有限，物联网驱动的边缘学习面临通信瓶颈、数据和设备异构性、非凸优化、隐私和安全问题以及动态操作环境带来的重大技术挑战。为了克服这些挑战，该项目通过人工智能和生物群体智能的整体集成，构建了分布式群体学习（DSL）的新框架。拟议的边缘学习 DSL 框架预计将有利于广泛的物联网应用，例如自主车队管理、大规模可穿戴电子产品、智能农业等。该项目还通过学生培训、劳动力发展、研究传播以及对少数群体和当地社区的推广，提供更广泛的社会影响。该项目的目标是开发一个高效的分布式学习框架，连贯地解决与带有设备的群体物联网相关的独特技术挑战通信、计算和数据的限制和资源限制，以及存在潜在链路故障、攻击和拓扑变化的复杂边缘环境。首先，通过将联邦学习与群体优化技术结合起来，建立了一个新的 DSL 框架。在理论支持下，开发了高效的信息提取和交换机制以及简约的传输方案，以实现模型更新的通信和计算的高效率。其次，为了应对实际物联网系统中的数据异构性、链路故障和恶意攻击，基于生成对抗网络、多工作器选择和模拟传输和聚合技术，开发了鲁棒的DSL技术。最后，为了在网络边缘以在线方式处理流数据，通过在DSL框架下设计自适应权重和探索利用策略来研究动态优化技术。这项研究的成果预计将有助于开发新的学习和优化工具，这些工具适合动态环境中大规模物联网的实时操作，并对统计学习、信号处理和无线通信产生技术影响。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。

项目成果

Robust Distributed Swarm Learning for Intelligent IoT

智能物联网的鲁棒分布式群体学习

• DOI: 10.1109/icc45041.2023.10278708
• 发表时间: 2023-05
• 期刊: IEEE International Conference on Communications
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi

Efficient Distributed Swarm Learning for Edge Computing

用于边缘计算的高效分布式群体学习

• DOI: 10.1109/icc45041.2023.10279508
• 发表时间: 2023-05
• 期刊: IEEE International Conference on Communications
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi

H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets

H-nobs：在异构数据集的分布式学习中实现经过认证的公平性和鲁棒性

• 发表时间: 2023-09
• 期刊: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023
• 作者: Zhou, Guanqiang;Xu, Ping;Wang, Yue;Tian, Zhi
• 通讯作者: Tian, Zhi

CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data

CB-DSL：非独立同分布的通信高效且拜占庭鲁棒的分布式群体学习

• DOI: 10.1109/tccn.2023.3312345
• 发表时间: 2023-09
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi