Distributed Swarm Learning for Edge Internet of Things

• URL: http://arxiv.org/abs/2403.20188v1
• Date: Fri, 29 Mar 2024 14:05:40 GMT
• Title: Distributed Swarm Learning for Edge Internet of Things
• Authors: Yue Wang, Zhi Tian, FXin Fan, Zhipeng Cai, Cameron Nowzari, Kai Zeng,
• Abstract summary: The rapid growth of the Internet of Things (IoT) has led to the widespread deployment of smart computation devices at wireless edge for machine learning tasks. This article explores the risks of swarm security, non-constrained wireless communication and privacy issues. It combines biological intelligence in a holistic manner to provide efficient solutions for large-scale IoT at the edge wireless networks.
• Score: 28.125744688546842
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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