Federated Weather Modeling on Sensor Data

2026-04-30

AI Summary

This work proposes a federated learning–based distributed weather modeling framework to address the challenge of sharing multi-source heterogeneous meteorological data under stringent privacy constraints. By enabling collaborative training across diverse sensing sources—including global ground weather stations, satellites, and IoT devices—without exchanging raw data, the approach facilitates joint optimization of deep learning models while preserving data privacy. The proposed framework significantly enhances the accuracy and robustness of weather forecasting and anomaly detection at both global and regional scales. Furthermore, it improves model generalization and geographic adaptability, demonstrating that effective cross-institutional collaboration in meteorological modeling is achievable without compromising sensitive data.

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.

Problem

• federated learning
• weather modeling
• sensor data
• data privacy
• distributed systems

Innovation

• Federated Learning
• Weather Modeling
• Sensor Data
• Privacy-Preserving
• Distributed Deep Learning