A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture
Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming...
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ftwit:oai:repository.wit.ie:3481 2023-11-05T03:41:36+01:00 A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh P. Balasubramaniam, Sasitharan 2021 text https://repository.wit.ie/3481/ https://ieeexplore.ieee.org/document/9312452 https://repository.wit.ie/3481/1/main.pdf https://doi.org/10.1109/TETC.2020.3048671 en eng https://repository.wit.ie/3481/1/main.pdf Vimalajeewa, Dixon and Kulatunga, Chamil and Berry, Donagh P. and Balasubramaniam, Sasitharan (2021) A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture. IEEE Transactions on Emerging Topics in Computing. (In Press) doi:10.1109/TETC.2020.3048671 Walton Institute for Information and Communications Systems Science Article PeerReviewed 2021 ftwit https://doi.org/10.1109/TETC.2020.3048671 2023-10-07T16:47:55Z Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (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 methodology, which 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. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with a large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequentially and parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques is still at an early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance. Article in Journal/Newspaper DML Waterford Institute of Technology: WIT Institutional Repository IEEE Transactions on Emerging Topics in Computing 1 1 |
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Waterford Institute of Technology: WIT Institutional Repository |
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English |
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Walton Institute for Information and Communications Systems Science |
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Walton Institute for Information and Communications Systems Science Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh P. Balasubramaniam, Sasitharan A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture |
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Walton Institute for Information and Communications Systems Science |
description |
Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (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 methodology, which 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. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with a large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequentially and parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques is still at an early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance. |
format |
Article in Journal/Newspaper |
author |
Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh P. Balasubramaniam, Sasitharan |
author_facet |
Vimalajeewa, Dixon Kulatunga, Chamil Berry, Donagh P. 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/3481/ https://ieeexplore.ieee.org/document/9312452 https://repository.wit.ie/3481/1/main.pdf https://doi.org/10.1109/TETC.2020.3048671 |
genre |
DML |
genre_facet |
DML |
op_relation |
https://repository.wit.ie/3481/1/main.pdf Vimalajeewa, Dixon and Kulatunga, Chamil and Berry, Donagh P. and Balasubramaniam, Sasitharan (2021) A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture. IEEE Transactions on Emerging Topics in Computing. (In Press) doi:10.1109/TETC.2020.3048671 |
op_doi |
https://doi.org/10.1109/TETC.2020.3048671 |
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IEEE Transactions on Emerging Topics in Computing |
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1781698050181300224 |