Rural AI: Serverless-Powered Federated Learning for Remote Applications

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 s...

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Bibliographic Details
Published in:IEEE internet computing 2023-03, Vol.27 (2), p.28-34
Main Authors: Patros, Panos, Ooi, Melanie, Huang, Victoria, Mayo, Michael, Anderson, Chris, Burroughs, Stephen, Baughman, Matt, Almurshed, Osama, Rana, Omer, Chard, Ryan, Chard, Kyle, Foster, Ian
Format: Article
Language:English
Subjects:
Artificial intelligence
Computational modeling
Computer architecture
computing continuum
cyber-physical infrastructure
Data models
Federated learning
Rural AI
Rural areas
Serverless computing
Urban areas
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Summary: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.
ISSN:1089-7801
1941-0131
DOI:10.1109/MIC.2022.3202764