Distributed Swarm Learning for Edge Internet of Things

2024/03/29 by Yue Wang, Wang, Yue, Zhi Tian +9 · 2 citations
Computer Science · #Energy Efficient Wireless Sensor Networks

paper · pdf · doi:10.48550/arxiv.2403.20188

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.

Cited by

• Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation
• Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data

Related

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