Distributed Swarm Learning for Edge Internet of Things
Yue Wang Zhi Tian FXin Fan Zhipeng Cai Cameron Nowzari Kai Zeng
The rapid growth of Internet of Things (IoT) has led to the widespread
deployment of smart IoT devices at wireless edge for collaborative machine
learning tasks, ushering in a new era of edge learning. With a huge number of
hardware-constrained IoT devices operating in resource-limited wireless
networks, edge learning encounters substantial challenges, including
communication and computation bottlenecks, device and data heterogeneity,
security risks, privacy leakages, non-convex optimization, and complex wireless
environments. To address these issues, this article explores a novel framework
known as distributed swarm learning (DSL), which combines artificial
intelligence and biological swarm intelligence in a holistic manner. By
harnessing advanced signal processing and communications, DSL provides
efficient solutions and robust tools for large-scale IoT at the edge of
wireless networks.