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

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IoT网络的边缘，以使用本地收集的数据执行协作机器学习任务，从而产生了边缘学习范式。由于物联网网络拥有海量的低成本边缘设备，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等方面的重大技术挑战。为了克服这些挑战，本项目通过人工智能和生物群智能的整体集成，构建了一个新的分布式群体学习(DSL)框架。拟议的边缘学习DSL框架预计将惠及广泛的物联网应用，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、研究传播和向少数族裔和当地社区推广，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，以协调一致地应对与群物联网相关的独特技术挑战，这些挑战包括通信、计算和数据方面的设备限制和资源限制，以及在可能存在链路故障、攻击和拓扑变化的复杂边缘环境中。首先，将联合学习与群体优化技术相结合，建立了一种新的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洁的传输方案，以实现模型更新的通信和计算的高效率。其次，针对物联网系统中存在的数据异构性、链路故障和恶意攻击等问题，提出了基于产生式对抗网络、多工作者选择和模拟传输聚合技术的健壮DSL技术。最后，为了在网络边缘在线处理流数据，通过设计DSL框架下的自适应权值和探测-利用策略，研究了动态优化技术。这项研究的成果预计将有助于为动态环境中大规模物联网的实时运营量身定做的学习和优化新工具，以及对统计学习、信号处理和无线通信的技术影响。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。

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.