Federated Weather Modeling on Sensor Data

Shengchao Chen; Guodong Long

doi:10.1007/978-3-319-23519-6_1719-1

Federated Weather Modeling on Sensor Data

Machine Learning 2026-05-04 v1

Authors: Shengchao Chen , Guodong Long

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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.

Keywords

federated learning time series forecasting remote sensing

Cite

@article{arxiv.2605.00322,
  title  = {Federated Weather Modeling on Sensor Data},
  author = {Shengchao Chen and Guodong Long},
  journal= {arXiv preprint arXiv:2605.00322},
  year   = {2026}
}

Comments

Accepted by Encyclopedia of GIS, this is an unedited version. Published version: https://link.springer.com/rwe/10.1007/978-3-319-23519-6_1719-1

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