  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.
