Distributed Learning Framework for Earth Observation on Multilayer Non-Terrestrial Networks

A novel Distributed Learning (DL) framework called Generalized Federated Split Transfer Learning (GFSTL) is proposed on a multilayer Non-Terrestrial Network (NTN) for Earth Observation (EO) missions. Through this, significant gaps in the literature related to the use of multilayer NTNs and Machine Learning (ML) perspectives are addressed. Multiple layers are considered to collect images and data at different sizes and resolutions, Transfer Learning (TL) to accelerate training and improve accuracy, Federated Learning (FL) to facilitate safe and secure collaboration, and Split Learning (SL) to optimize resource use and preserve privacy. The proposed framework is expected to overcome limitations in existing techniques, offering enhanced accuracy, privacy preservation, and scalability.