  The classification of weather data involves categorizing meteorological
phenomena into classes, thereby facilitating nuanced analyses and precise
predictions for various sectors such as agriculture, aviation, and disaster
management. This involves utilizing machine learning models to analyze large,
multidimensional weather datasets for patterns and trends. These datasets may
include variables such as temperature, humidity, wind speed, and pressure,
contributing to meteorological conditions. Furthermore, it's imperative that
classification algorithms proficiently navigate challenges such as data
imbalances, where certain weather events (e.g., storms or extreme temperatures)
might be underrepresented. This empirical study explores data augmentation
methods to address imbalanced classes in tabular weather data in centralized
and federated settings. Employing data augmentation techniques such as the
Synthetic Minority Over-sampling Technique or Generative Adversarial Networks
can improve the model's accuracy in classifying rare but critical weather
events. Moreover, with advancements in federated learning, machine learning
models can be trained across decentralized databases, ensuring privacy and data
integrity while mitigating the need for centralized data storage and
processing. Thus, the classification of weather data stands as a critical
bridge, linking raw meteorological data to actionable insights, enhancing our
capacity to anticipate and prepare for diverse weather conditions.
