Federated Learning for Distributed Weather Forecasting: A Practical Approach on Real Multidimensional Georeferenced Data

The rapid advancement of technology-particularly in machine learning and data availability has led to an increasing demand for reducing communication overhead and operational costs. Edge computing is one of the fastest-growing fields, enabling cost reduction and improving communication. Simultaneously, the advancement of machine learning and the availability of data are both accelerating. Federated learning (FL) addresses not only privacy concerns but also issues related to cost and communication efficiency. This paper presents an application of a federated learning framework to improve weather forecast accuracy through the collaborative analysis of distributed data, while ensuring data confidentiality and computational efficiency. To replicate the federated environment, we developed an architecture that combines real-time data collection using the Signal K server, containerization using Docker, and a Hadoop cluster on Microsoft Azure. We evaluated the performance of a Transformer and a Crossformer, demonstrating the effectiveness of both models in this context, with the Crossformer showing superior performance in managing spatiotemporal dependencies for forecasting. Our experimental results indicate an important improvement in reducing the error with respect to previous methods, achieving a Mean Absolute Error (MAE) of 0.144 for the Crossformer and 0.232 for the Transformer, highlighting the potential of FL and advanced deep learning architectures in managing sensitive data in distributed scenarios, in line with previous research trends. This study proposes a robust and scalable approach, opening new perspectives for future applications in cooperative and secure learning.