用联邦学习联合多地传感器数据，提升天气预报精度。
Federated Weather Modeling on Sensor Data
- 多源传感器数据通过联邦学习协作训练模型
- 无需共享原始数据即可提升预报准确率
- 适合关注隐私保护的气象机构与物联网应用
基于联邦学习的分布式天气建模系统，使地面气象站、卫星和物联网设备等多源传感器数据在不共享原始数据的前提下协同训练深度学习模型。该方法在保障数据隐私与安全的同时，利用分布广泛、多样化的数据集，提升了全球或区域尺度天气预测及异常检测任务的准确性与鲁棒性。
原文摘要 · Abstract (English)
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.
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