Cross-Image Federated Learning for Hyperspectral Image Classification

This research proposes a federated learning (a decentralized machine learning approach where multiple computers train a model together without sharing raw data) approach for classifying hyperspectral images (images that capture many light wavelengths to reveal details about Earth's surface) across multiple satellite sources. The method addresses two key challenges: improving how well each local computer learns by sharing relevant features with others, and handling bias that arises when different computers have uneven amounts of training data.