A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture
Advanced distributed analytics facilitate to make the services smarter for a wider range of data-driven applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from data. Centralized data analytic services are becoming...
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ftwit:oai:repository.wit.ie:3683 2023-05-15T16:01:41+02:00 A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh Balasubramaniam, Sasitharan 2021 https://repository.wit.ie/3683/ unknown Vimalajeewa, Dixon and Kulatunga, Chamil and Berry, Donagh and Balasubramaniam, Sasitharan (2021) A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture. IEEE Transactions on Emerging Topics in Computing. ISSN 2168-6750 (In Press) Article PeerReviewed 2021 ftwit 2023-02-02T23:58:22Z Advanced distributed analytics facilitate to make the services smarter for a wider range of data-driven applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from data. Centralized data analytic services are becoming infeasible due to limitations in both the ICT infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML concept, enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there need to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Therefore, in this paper, we propose a Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Then its predictive performance is evaluated under sequential- and parallel-updating based FL in a smart farming context, and specifically for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytic platforms to employ sustainable farming practices. The FL-NNPLS approach performs and compares well with a centralized approach and has state-of-the-art performance. Article in Journal/Newspaper DML Waterford Institute of Technology: WIT Institutional Repository |
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Waterford Institute of Technology: WIT Institutional Repository |
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description |
Advanced distributed analytics facilitate to make the services smarter for a wider range of data-driven applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from data. Centralized data analytic services are becoming infeasible due to limitations in both the ICT infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML concept, enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there need to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Therefore, in this paper, we propose a Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Then its predictive performance is evaluated under sequential- and parallel-updating based FL in a smart farming context, and specifically for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytic platforms to employ sustainable farming practices. The FL-NNPLS approach performs and compares well with a centralized approach and has state-of-the-art performance. |
format |
Article in Journal/Newspaper |
author |
Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh Balasubramaniam, Sasitharan |
spellingShingle |
Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh Balasubramaniam, Sasitharan A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
author_facet |
Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh Balasubramaniam, Sasitharan |
author_sort |
Vimalajeewa, Dixon |
title |
A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
title_short |
A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
title_full |
A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
title_fullStr |
A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
title_full_unstemmed |
A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture |
title_sort |
service-based joint model used for distributed learning : application for smart agriculture |
publishDate |
2021 |
url |
https://repository.wit.ie/3683/ |
genre |
DML |
genre_facet |
DML |
op_relation |
Vimalajeewa, Dixon and Kulatunga, Chamil and Berry, Donagh and Balasubramaniam, Sasitharan (2021) A Service-based Joint Model Used for Distributed Learning : Application for Smart Agriculture. IEEE Transactions on Emerging Topics in Computing. ISSN 2168-6750 (In Press) |
_version_ |
1766397441952710656 |