Rural AI: Serverless-powered federated learning for remote applications

Patros, Panos, Ooi, Melanie, Huang, Victoria, Mayo, Michael, Anderson, Chris, Burroughs, Stephen, Baughman, Matt, Almurshed, Osama, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Chard, Ryan, Chard, Kyle and Foster, Ian 2023. Rural AI: Serverless-powered federated learning for remote applications. IEEE Internet Computing 27 (2) , pp. 28-34. 10.1109/MIC.2022.3202764

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Abstract

With increasing connectivity to support digital services in urban areas, there is a realization that demand for offering similar capability in rural communities is still limited. To unlock the potential of Artificial Intelligence (AI) within rural economies, we propose Rural AI—the mobilization of serverless computing to enable AI in austere environments. Inspired by problems observed in New Zealand, we analyze major challenges in agrarian communities and define their requirements. We demonstrate a proof-of-concept Rural AI system for cross-field pasture weed detection that illustrates the capabilities serverless computing offers to traditional federated learning.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1089-7801
Date of First Compliant Deposit:	23 November 2022
Date of Acceptance:	30 September 2022
Last Modified:	20 Nov 2024 00:00
URI:	https://orca.cardiff.ac.uk/id/eprint/154447

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