  Low-Earth orbit (LEO) satellites have been prosperously deployed for various
Earth observation missions due to its capability of collecting a large amount
of image or sensor data. However, traditionally, the data training process is
performed in the terrestrial cloud server, which leads to a high transmission
overhead. With the recent development of LEO, it is more imperative to provide
ultra-dense LEO constellation with enhanced on-board computation capability.
Benefited from it, we have proposed a collaborative federated learning for low
Earth orbit (FELLO). We allocate the entire process on LEOs with low payload
inter-satellite transmissions, whilst the low-delay terrestrial gateway server
(GS) only takes care for initial signal controlling. The GS initially selects
an LEO server, whereas its LEO clients are all determined by clustering
mechanism and communication capability through the optical inter-satellite
links (ISLs). The re-clustering of changing LEO server will be executed once
with low communication quality of FELLO. In the simulations, we have
numerically analyzed the proposed FELLO under practical Walker-based LEO
constellation configurations along with MNIST training dataset for
classification mission. The proposed FELLO outperforms the conventional
centralized and distributed architectures with higher classification accuracy
as well as comparably lower latency of joint communication and computing.
