Distributed Swarm Learning for Edge Internet of Things

TL;DR

This paper introduces distributed swarm learning (DSL), a novel framework combining AI and swarm intelligence to enhance large-scale IoT edge learning amidst resource constraints, heterogeneity, and security challenges.

Contribution

The paper proposes a new distributed swarm learning framework that integrates AI with biological swarm intelligence to address key challenges in IoT edge learning.

Findings

DSL offers efficient communication and computation solutions.

DSL enhances robustness against security and privacy issues.

It effectively manages heterogeneity in IoT devices.

Abstract

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.