Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of data collected by sensors.

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices.

Satellite image based land cover classification, which falls under the category of semantic segmentation, is critical for many global and environmental applications. Deep learning has been proven to be excellent in semantic segmentation. However, mainstream neural networks formed by connecting high-to-low convolutions in series are prone to losing image information, which affects the accuracy of semantic segmentation.