20.500.12556/DKUM-88287 A review of federated learning in agriculture 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. federated learning agriculture architecture data partitioning federation scal aggregation algorithms communication bottleneck zvezno učenje kmetijstvo arhitektura particioniranje podatkov algoritmi združevanja true true true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/sensors © 2023 by the authors Članek v reviji 2024-04-13 04:06:04 2024-06-05 11:17:35 2024-09-17 12:22:15 0000-00-00 00:00:00 2023 0 0 20 str. iss. 23, [article no.] 9566 Vol. 23 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-10-11 2023-11-29 2023-12-02 004.8 179436547 10.3390/s23239566 1424-8220 sensors-23-09566.pdf sensors-23-09566.pdf 1 27F3FBAC5BD6C26959AC1CCE3B21B98F 44215b4eec3d0d191f0aaa0e90fffa5eb3ccaa6452f0ae838c7b80dd69c707ab 96a76014-231c-11ef-9e9b-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=180212 https://www.mdpi.com/1424-8220/23/23/9566 0 9f45e7a2-231c-11ef-9e9b-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=180213 Fakulteta za naravoslovje in matematiko Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0