论文标题
在边缘的物联网分布式群体学习：人工智能符合生物智能的地方
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
论文作者
论文摘要
随着多功能物联网（IoT）服务的扩散，智能物联网设备越来越多地在无线网络的边缘部署，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。由于设备限制和资源限制，大型物联网设备之间的边缘学习面临着由通信瓶颈，数据和设备异质性，非凸优化，隐私和安全问题以及动态环境引起的主要技术挑战。为了克服这些挑战，本文通过人工智能和生物群智能的整体整合研究了分布式群学习（DSL）的新框架。 DSL利用高效且健壮的信号处理和通信技术，为在边缘无线环境中为实时物联网的实时操作而定制的学习和优化的新工具，这将使广泛的Edge IoT应用程序受益。
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm. Due to device restrictions and resource constraints, edge learning among massive IoT devices faces major technical challenges caused by the communication bottleneck, data and device heterogeneity, non-convex optimization, privacy and security concerns, and dynamic environments. To overcome these challenges, this article studies a new framework of distributed swarm learning (DSL) through a holistic integration of artificial intelligence and biological swarm intelligence. Leveraging efficient and robust signal processing and communication techniques, DSL contributes to novel tools for learning and optimization tailored for real-time operations of large-scale IoT in edge wireless environments, which will benefit a wide range of edge IoT applications.
