Federated Weather Modeling on Sensor Data

TL;DR

This paper presents a federated learning approach for weather modeling that allows multiple sensor sources to collaboratively train models without sharing raw data, enhancing privacy and model robustness.

Contribution

It introduces a federated learning framework tailored for weather data from diverse sensors, improving accuracy and privacy in weather prediction tasks.

Findings

Enhanced weather forecasting accuracy through federated training.

Preserved data privacy by avoiding raw data sharing.

Leveraged diverse sensor data for robust model performance.

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.