Abstract:Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including ground weather stations, satellites and IoT devices, to collaboratively train deep learning models without sharing raw data. This method safeguards data privacy and security while leverages diverse, geographically distributed datasets to improve the accuracy and robustness of global/regional weather modeling tasks such as forecasting and anomaly detection.
| Comments: | Accepted by Encyclopedia of GIS, this is an unedited version. Published version: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00322 [cs.LG] |
| (or arXiv:2605.00322v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00322 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham (2026) |
| Related DOI: | https://doi.org/10.1007/978-3-319-23519-6_1719-1
DOI(s) linking to related resources |
From: Shengchao Chen [view email]
[v1]
Fri, 1 May 2026 01:01:13 UTC (695 KB)
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