Federated Weather Modeling on Sensor Data
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915971896180736 |
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| author | Chen, Shengchao Long, Guodong |
| author_facet | Chen, Shengchao Long, Guodong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00322 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Federated Weather Modeling on Sensor Data Chen, Shengchao Long, Guodong Machine Learning 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. |
| title | Federated Weather Modeling on Sensor Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.00322 |