{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T12:12:04Z","timestamp":1768997524096,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T00:00:00Z","timestamp":1746921600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T00:00:00Z","timestamp":1746921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Tampere University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task.    <\/jats:p>","DOI":"10.1007\/s44163-025-00288-8","type":"journal-article","created":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T13:42:43Z","timestamp":1746970963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated transfer learning for distributed drought stage prediction"],"prefix":"10.1007","volume":"5","author":[{"given":"Muhammad Owais","family":"Raza","sequence":"first","affiliation":[]},{"given":"Aqsa","family":"Umar","sequence":"additional","affiliation":[]},{"given":"Jawad","family":"Rasheed","sequence":"additional","affiliation":[]},{"given":"Tunc","family":"Asuroglu","sequence":"additional","affiliation":[]},{"given":"Shtwai","family":"Alsubai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,11]]},"reference":[{"key":"288_CR1","doi-asserted-by":"publisher","first-page":"103210","DOI":"10.1016\/j.ijdrr.2022.103210","volume":"80","author":"MM Ahmad","year":"2022","unstructured":"Ahmad MM, Yaseen M, Saqib SE. Climate change impacts of drought on the livelihood of dryland smallholders: implications of adaptation challenges. Int J Disaster Risk Reduct. 2022;80:103210.","journal-title":"Int J Disaster Risk Reduct"},{"key":"288_CR2","unstructured":"Calow R, MacDonald A, Nicol A, Robins N, Kebede S. The struggle for water: drought, water security and rural livelihoods. 2006."},{"key":"288_CR3","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ijdrr.2016.12.012","volume":"21","author":"M Keshavarz","year":"2017","unstructured":"Keshavarz M, Maleksaeidi H, Karami E. Livelihood vulnerability to drought: a case of rural Iran. Int J Disaster Risk Reduct. 2017;21:223\u201330.","journal-title":"Int J Disaster Risk Reduct"},{"key":"288_CR4","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.wace.2015.10.001","volume":"11","author":"KL Ebi","year":"2016","unstructured":"Ebi KL, Bowen K. Extreme events as sources of health vulnerability: drought as an example. 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