Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Ooi, Melanie Po-Leen, Sohail, Shaleeza, Huang, Victoria Guiying, Hudson, Nathaniel, Baughman, Matt, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Hinze, Annika, Chard, Kyle, Chard, Ryan, Foster, Ian, Spyridopoulos, Theodoros ORCID: https://orcid.org/0000-0001-7575-9909 and Nagra, Harshaan 2023. Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications. IEEE Instrumentation & Measurement Magazine 26 (9) , pp. 21-31. 10.1109/MIM.2023.10328671

Preview

PDF - Accepted Post-Print Version Download (895kB) | Preview

Abstract

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1094-6969
Date of First Compliant Deposit:	11 December 2023
Date of Acceptance:	10 November 2023
Last Modified:	13 Dec 2023 03:06
URI:	https://orca.cardiff.ac.uk/id/eprint/164684

Actions (repository staff only)

Edit Item