Balancing centralisation and decentralisation in federated learning for Earth Observation-based agricultural predictions

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Cowlishaw, Robert and Longépé, Nicolas and Riccardi, Annalisa (2025) Balancing centralisation and decentralisation in federated learning for Earth Observation-based agricultural predictions. Scientific Reports, 15. 10454. ISSN 2045-2322 (https://doi.org/10.1038/s41598-025-94244-2)

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Abstract

Crop yield prediction using Earth Observation data presents challenges due to the diverse data modalities and the limited availability of relevant datasets, which are often proprietary or private. Decentralised federated learning has been proposed as a solution to address these privacy concerns. However, the performance of federated learning is significantly influenced by the number of clients and the distribution of data among them. This study investigates the impact of aggregation levels on federated learning using a proxy model trained on crop type data derived from Copernicus Sentinel-2 images. The analysis also includes an examination of the current and future distributions of crop yield datasets to determine the optimal aggregation levels for effective federated learning. The findings highlight that dataset size directly affects the learning outcomes and the degree of privacy that can be maintained. Differential privacy techniques are also discussed in relation to the challenges posed by varying dataset sizes.

ORCID iDs

Cowlishaw, Robert ORCID logoORCID: https://orcid.org/0009-0001-7052-4913, Longépé, Nicolas and Riccardi, Annalisa ORCID logoORCID: https://orcid.org/0000-0001-5305-9450;
CORE (COnnecting REpositories)