With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

In applications such as environmental monitoring, algorithms and deep learning-based
methods using synthetic aperture radar (SAR) and electro-optical (EO) data have been …

Advances in deep learning have revolutionized medical image segmentation, facilitating the
precise delineation of complex anatomical structures. The scarcity of annotated training …

Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

The training of diffusion-based models for image generation is predominantly controlled by a
select few Big Tech companies, raising concerns about privacy, copyright, and data authority …

For proper planning of urban infrastructures such as road networks, pipelines, and other
linear engineering structures, it is necessary to construct precise land use and land cover …

Credit risk assessment is an important process in bank financial risk management.
Traditional machine-learning methods cannot solve the problem of data islands and the high …

This paper introduces a novel approach method for decentralized land cover and land use
classification, utilizing federated learning in conjunction with Convolutional Neural Networks …

JAIST Repository Page 1 Japan Advanced Institute of Science and Technology JAIST
Repository https://dspace.jaist.ac.jp/ Title 医療画像分割の向上:モデルの精度,プライバシー,および …