Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
<|reference_start|>Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas: The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach.<|reference_end|>
@article{díaz2024personalized,
    title={Personalized Federated Learning for improving radar based precipitation
  nowcasting on heterogeneous areas},
    author={Judith S'ainz-Pardo D'iaz, Mar'ia Castrillo, Juraj Bartok, Ignacio
  Heredia Cach'a, Irina Malkin Ond'ik, Ivan Martynovskyi, Khadijeh Alibabaei,
  Lisana Berberi, Valentin Kozlov and 'Alvaro L'opez Garc'ia},
    journal={arXiv preprint arXiv:2408.05761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05761},
    primaryClass={cs.LG physics.ao-ph}
}