| Title: | A review of federated learning in agriculture |
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| Authors: | ID Rizman Žalik, Krista (Author)
ID Žalik, Mitja (Author) |
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| Files: |  sensors-23-09566.pdf (839,33 KB)
MD5: 27F3FBAC5BD6C26959AC1CCE3B21B98F
 https://www.mdpi.com/1424-8220/23/23/9566
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.02 - Review Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems. We compare the types of data partitioning and types of FL (horizontal partitioning and horizontal FL, vertical partitioning and vertical FL, and hybrid partitioning and transfer FL), architectures (centralized and decentralized), levels of federation (cross-device and cross-silo), and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. We also briefly review how the communication challenge is solved by different approaches. This work is useful for gaining an overview of the FL techniques used in agriculture and the progress made in this field. |
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| Keywords: | federated learning, agriculture, architecture, data partitioning, federation scal, aggregation algorithms, communication bottleneck |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 11.10.2023 |
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| Article acceptance date: | 29.11.2023 |
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| Publication date: | 02.12.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | 20 str. |
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| Numbering: | Vol. 23, iss. 23, [article no.] 9566 |
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| PID: | 20.500.12556/DKUM-88287  |
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| UDC: | 004.8 |
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| ISSN on article: | 1424-8220 |
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| COBISS.SI-ID: | 179436547 |
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| DOI: | 10.3390/s23239566 |
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| Copyright: | © 2023 by the authors |
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| Publication date in DKUM: | 05.06.2024 |
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| Views: | 346 |
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| Downloads: | 90 |
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| Metadata: |    |
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| Categories: | Misc.
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