{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:20:54Z","timestamp":1781306454314,"version":"3.54.1"},"reference-count":78,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Slovene Research Agency","award":["J2-4458"],"award-info":[{"award-number":["J2-4458"]}]},{"name":"Slovene Research Agency","award":["P2-0041"],"award-info":[{"award-number":["P2-0041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23239566","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T13:45:49Z","timestamp":1701524749000},"page":"9566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["A Review of Federated Learning in Agriculture"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6980-4523","authenticated-orcid":false,"given":"Krista Rizman","family":"\u017dalik","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia"},{"name":"Faculty of Natural Sciences and Mathematics, University of Maribor, 2000 Maribor, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitja","family":"\u017dalik","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1147\/rd.33.0210","article-title":"Some Studies in Machine Learning Using the Game of Checkers","volume":"3","author":"Samuel","year":"1959","journal-title":"IBM J. Res. Dev."},{"key":"ref_2","unstructured":"Ethem, A. (2020). Introduction to Machine Learning, MIT. [4th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., and Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21.","DOI":"10.3390\/s21113758"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","unstructured":"Schmidhuber, J. (2022). Annotated History of Modern AI and Deep Learning. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105887","DOI":"10.1016\/j.knosys.2020.105887","article-title":"CNN-based image recognition for topology optimization","volume":"198","author":"Lee","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jain, L.C., and Medsker, L.R. (1999). Recurrent Neural Networks: Design and Applications, CRC Press, Inc.","DOI":"10.1201\/9781420049176"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85714","DOI":"10.1109\/ACCESS.2020.2991734","article-title":"An Overview on Edge Computing Research","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2017.9","article-title":"The emergence of edge computing","volume":"50","author":"Satyanarayanan","year":"2017","journal-title":"Computer"},{"key":"ref_13","first-page":"2191","article-title":"Edge-Cloud Computing for Internet of Things Data Analytics: Embedding Intelligence in the Edge with Deep Learning","volume":"17","author":"Ghosh","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","article-title":"Convergence of Edge Computing and Deep Learning: A Comprehensive Survey","volume":"22","author":"Wang","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Morais, R., Mendes, J., Silva, R., Silva, N., Sousa, J.J., and Peres, E. (2021). A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices. Agriculture, 11.","DOI":"10.3390\/agriculture11070619"},{"key":"ref_16","first-page":"31","article-title":"Application of remote sensing methods in agriculture","volume":"11","author":"Piekarczyk","year":"2016","journal-title":"Commun. Biometry Crop Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"903230","DOI":"10.3389\/fsufs.2022.903230","article-title":"Protecting farmers\u2019 data privacy and confidentiality: Recommendations and considerations","volume":"6","author":"Kaur","year":"2022","journal-title":"Front. Sustain. Food Syst."},{"key":"ref_18","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., and Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv."},{"key":"ref_19","unstructured":"McMahan, H.B., Moore ERamage, D., Hampson, S., and Arcas, B.A. (2017, January 20\u201322). Communication efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, FL, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and Open Problems in Federated Learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","article-title":"A survey on security and privacy of federated learning","volume":"115","author":"Mothukuri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","first-page":"50","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124682","DOI":"10.1109\/ACCESS.2021.3111118","article-title":"Challenges, applications and design aspects of federated learning: A survey","volume":"9","author":"Rahman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1146\/annurev-psych-010418-102803","article-title":"How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses","volume":"70","author":"Siddaway","year":"2019","journal-title":"Ann. Rev. Psychol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"140699","DOI":"10.1109\/ACCESS.2020.3013541","article-title":"Federated learning: A survey on enabling technologies, protocols, and applications","volume":"8","author":"Aledhari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"Arivazhagan, G.M., Aggarwal, V., Singh, A.K., and Choudhary, S. (2019). Federated Learning with Personalization Layers. arXiv."},{"key":"ref_28","unstructured":"Karimireddy, S.P., Jaggi, M., Kale, S., Mohri, M., Reddi, S.J., Stich, S.U., and Suresh, A.T. (2020). Mime: Mimicking centralized stochastic algorithms in federated learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s10115-022-01664-x","article-title":"From distributed machine learning to federated learning: A survey","volume":"64","author":"Liu","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"ref_30","unstructured":"Liu, Y., Kang, Y., Li, L., Zhang, X., Cheng, Y., Chen, T., Hong, M., and Yang, Q. (2019). Scanning Electron Microsc Meet at, Cambridge University Press."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, K., Song, Z., Zhang, Y., Zhou, Y., Sun, X., and Wang, J. (2021, January 22\u201328). Model optimization method based on vertical federated learning. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401521"},{"key":"ref_32","unstructured":"Chen, T., Jin, X., Sun, Y., and Yin, W. (2020). VAFL: A method of vertical asynchronous federated learning. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: A federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"ref_34","unstructured":"Zhang, X., Yin, W., Hong, M., and Chen, T. (2021). Hybrid Federated Learning: Algorithms and Implementation. arXiv."},{"key":"ref_35","unstructured":"Liang, X., Liu, Y., Chen, T., Liu, M., and Yang, Q. (2019). Federated transfer reinforcement learning for autonomous driving. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","article-title":"Fedsteg: A federated transfer learning framework for secure image steganalysis","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_37","unstructured":"Beltr\u00e1n, E.T., P\u00e9rez, M.Q., S\u00e1nchez, P.M., Bernal, S.L., Bovet, G., P\u00e9rez, M.G., P\u00e9rez, G.M., and Celdr\u00e1n, A.H. (2022). Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges. arXiv."},{"key":"ref_38","unstructured":"Yuan, L., Sun, L., Yu, P.S., and Wang, Z. (2023). Decentralized Federated Learning: A Survey and Perspective. arXiv."},{"key":"ref_39","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"Delange","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1109\/TII.2021.3116132","article-title":"Blockchainempowered decentralized horizontal federated learning for 5G-enabled UAVs","volume":"18","author":"Feng","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_41","unstructured":"S\u00e1nchez, P.M., Celdr\u00e1n, A.H., Beltr\u00e1n, E.T.M., Demeter, D., Bovet, G., P\u00e9rez, G.M., and Stille, B. (2022). Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/JIOT.2021.3078543","article-title":"Decentralized federated learning via mutual knowledge transfer","volume":"9","author":"Li","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5986","DOI":"10.1109\/JIOT.2019.2956615","article-title":"Communication-efficient federated learning for wireless edge intelligence in iot","volume":"7","author":"Mills","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_44","unstructured":"Lafferty, J., Williams, C., Taylor, J.S., Zemel, R., and Culotta, A. (2010). Advances in Neural Information Processing Systems, Curran Associates, Inc.. Available online: https:\/\/proceedings.neurips.cc\/paper\/2010\/file\/abea47ba24142ed16b7d8fbf2c740e0d-Paper.pdf."},{"key":"ref_45","first-page":"429","article-title":"Federated Optimization in Heterogeneous Net-works","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_46","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zhou, Y., Wu, D., Chen, X., Chen, M., Li, C., and Sheng, Q.Z. (2021, January 10\u201313). P-FedAvg: Parallelizing federated learning with theoretical guarantees. Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488877"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"209191","DOI":"10.1109\/ACCESS.2020.3038287","article-title":"EdgeFed: Optimized Federated Learning Based on Edge Computing","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Huang, C., Huang, J., and Liu, X. (2022). Cross-Silo Federated Learning: Challenges and Opportunities. arXiv."},{"key":"ref_50","unstructured":"Han, J., Han, Y., Huang, G., and Ma, Y. (2022). DeFL: Decentralized weight aggregation for cross-silo federated learning. arXiv."},{"key":"ref_51","first-page":"28663","article-title":"Breaking the centralized barrier for cross-device federated learning","volume":"Volume 34","author":"Ranzato","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_52","unstructured":"Manoj, T., Makkithaya, K., and Narendra, V.G. (2022, January 11\u201313). A Federated Learning-Based Crop Yield Prediction for Agricultural Production Risk Management. Proceedings of the 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MM.2021.3112476","article-title":"PEFL: Deep privacy-encoding-based federated learning framework for smart agriculture","volume":"42","author":"Kumar","year":"2021","journal-title":"IEEE Micro"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106648","DOI":"10.1016\/j.compag.2021.106648","article-title":"The role of cross-silo federated learning in facilitating data sharing in the agri-food sector","volume":"193","author":"Durrant","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_55","unstructured":"Antico, T.M., Moreira LF, R., and Moreira, R. (2022). Anais do XIX Encontro Nacional de Intelig\u00eancia Artificial e Computacional, Sociedade Brasileira de Computa\u00e7\u00e3o."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mao, A., Huang, E., Gan, H., and Liu, K. (2022). FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors. Animals, 12.","DOI":"10.20944\/preprints202206.0306.v1"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Khan, F.S., Khan, S., Mohd MN, H., Waseem, A., Khan MN, A., Ali, S., and Ahmed, R. (2022, January 27\u201328). Federated learning-based UAVs for the diagnosis of Plant Diseases. Proceedings of the 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICEET56468.2022.10007133"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jpdc.2022.03.003","article-title":"FELIDS: Federated learning-based intrusion detection system for agricultural IIn Proceedings of thenternet of Things","volume":"165","author":"Friha","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Abu-Khadrah, A., Mohd, A., and Jarrah, M. (2023). An Amendable Multi-Function Control Method using Federated Learning for Smart Sensors in Agricultural Production Improvements. ACM Trans. Sen. Netw., Preprint.","DOI":"10.1145\/3582011"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yu, C., Shen, S., Zhang, K., Zhao, H., and Shi, Y. (2022, January 10\u201313). Energy-aware device scheduling for joint federated learning in edge-assisted internet of agriculture things. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA.","DOI":"10.1109\/WCNC51071.2022.9771547"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"100277","DOI":"10.1016\/j.atech.2023.100277","article-title":"Federated Learning: Crop classification in a smart farm decentralised network","volume":"5","author":"Idoje","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3523811","DOI":"10.1109\/TIM.2022.3201937","article-title":"Multiple Diseases and Pests Detection Based on Federated Learning and Improved Faster R-CNN","volume":"71","author":"Deng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_63","first-page":"19","article-title":"Federated learning: Applications, challenges and future directions","volume":"18","author":"Bharati","year":"2022","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.comcom.2023.03.002","article-title":"Joint think locally and globally: Communication-efficient federated learning with feature-aligned filter selection","volume":"203","author":"Yang","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"6831","DOI":"10.1109\/TNNLS.2021.3083655","article-title":"Communication-censored distributed stochastic gradient descent","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_66","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K.-R., and Samek, W. (2019). Robust and communication-efficient federated learning from non-iid data. arXiv."},{"key":"ref_67","unstructured":"Rothchild, D., Panda, A., Ullah, E., Ivkin, N., Stoica, I., Braverman, V., Gonzalez, J., and Arora, R. (2020, January 13\u201318). Fetchsgd: Communication-efficient federated learning with sketching. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Li, S., Qi, Q., Wang, J., Sun, H., Li, Y., and Yu, F.R. (2020, January 7\u201311). GGS: General Gradient Sparsification for Federated Learning in Edge Computing. Proceedings of the ICC 2020\u20142020 IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9148987"},{"key":"ref_69","unstructured":"Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R. (2020, January 26\u201328). Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization. Proceedings of the International Conference on Artificial Intelligence and Statistics, Online."},{"key":"ref_70","unstructured":"Amiri, M.M., Gunduz, D., Kulkarni, S.R., and Poor, H.V. (2020). Federated learning with quantized global model updates. arXiv."},{"key":"ref_71","unstructured":"Liu, L., Zhang, J., Song, S., and Letaief, K.B. (2021). Hierarchical quantized federated learning: Convergence analysis and system design. arXiv."},{"key":"ref_72","unstructured":"Haddadpour, F., Kamani, M.M., Mokhtari, A., and Mahdavi, M. (2021, January 13\u201315). Federated learning with compression: Unified analysis and sharp guarantees. Proceedings of the International Conference on Artificial Intelligence and Statistics 2021, Virtual."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yang, T.J., Xiao, Y., Motta, G., Beaufays, F., Mathews, R., and Chen, M. (2022). Online Model Compression for Federated Learning with Large Models. arXiv.","DOI":"10.1109\/ICASSP49357.2023.10097124"},{"key":"ref_74","unstructured":"Malekijoo, A., Fadaeieslam, M.J., Malekijou, H., Homayounfar, M., Alizadeh-Shabdiz, F., and Rawassizadeh, R. (2021). FEDZIP: A Compression Framework for Communication-Efficient Federated Learning. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives. Electronics, 12.","DOI":"10.3390\/electronics12102287"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","article-title":"Model aggregation techniques in federated learning: A comprehensive survey","volume":"150","author":"Qi","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"10639","DOI":"10.1109\/JIOT.2021.3050163","article-title":"Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach","volume":"8","author":"Wu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_78","unstructured":"Fernandez, J.D., Brennecke, M., Rieger, A., Barbereau, T., and Fridgen, G. (2023). Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:36:29Z","timestamp":1760132189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":78,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239566"],"URL":"https:\/\/doi.org\/10.3390\/s23239566","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}