Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

[thumbnail of Rural_AI__Federated_Learning_on_Serverless_Edge__Magazine_Paper_ (5).pdf]
Preview
PDF - Accepted Post-Print Version
Download (1MB) | Preview

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: 09 Nov 2023 23:41
URI: https://orca.cardiff.ac.uk/id/eprint/154447

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics