Model pruning enables localized and efficient federated learning for yield forecasting and data sharing

Li, Andy HanouMarkovic, MilanEdwards, PeteLeontidis, Georgios2023-12-072023-12-072024-05-15Li, A H, Markovic, M, Edwards, P & Leontidis, G 2024, 'Model pruning enables localized and efficient federated learning for yield forecasting and data sharing', Expert Systems with Applications, vol. 242, 122847, pp. 1-12. https://doi.org/10.1016/j.eswa.2023.1228470957-4174ORCID: /0000-0002-4527-9186/work/148505193ORCID: /0000-0001-6671-5568/work/148506881https://hdl.handle.net/2164/22372The work described here was funded by the EPSRC ‘Enhancing Agri-Food Transparent Sustainability’ (EATS) project, United Kingdom (grant number: EP/V042270/1) and by a University of Aberdeen Ph.D. studentship, United Kingdom. We also thank the University of Aberdeen’s HPC facility Maxwell. Open Access via the Elsevier Agreement121315645engSubstantive connection via an eligible employment contractSDG 13 - Climate ActionSDG 2 - Zero Hunger2040 Data and Artificial IntelligenceFederated LearningPruningDeep Learningyield forecastingQA75 Electronic computers. Computer scienceArtificial IntelligenceQA75Model pruning enables localized and efficient federated learning for yield forecasting and data sharingJournal article10.1016/j.eswa.2023.122847https://www.sciencedirect.com/science/article/pii/S0957417423033493242