# Towards Client Selection in Satellite Federated Learning
###### Abstract  
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these data. However, when applying FL in satellite networks, it is necessary to consider the unique challenges brought by satellite networks, which include satellite communication, computational ability, and the interaction relationship between clients and servers. This study focuses on the setting of parameter servers (PSs), whether terrestrial or extraterrestrial, and explores the challenges of implementing a satellite federated learning (SFL) algorithm equipped with client selection (CS). We proposed an index called \"client affinity\" to measure the contribution of the client to the global model, and a CS algorithm was designed in this way. A series of experiments have indicated the advantage of our SFL paradigm--that satellites function as the PS--and the availability of our CS algorithm. Our method can halve the convergence time of both FedSat and FedSpace, and improve the precision of the models by up to 80%. federated learning; client selection; satellite edge computing +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-00000-00  
and the obsolescence of local models. They calculated the variable satellite connectivity based on the satellite orbits and Earth's rotation to determine the global model aggregate schedule. On this basis, [PERSON] et al. [17] proposed FedISL, which uses inter-satellite links to reduce the delay of FL. Furthermore, they considered whether satellites can complete local model training during the communication window and designed a scheduling algorithm, FedSatSchedule [18]. Due to the intermittent connectivity between satellites and GSs, stale gradients and unstable learning in SFL are challenges. Hence, [PERSON] et al. [19] proposed FedGSM, a novel asynchronous FL algorithm that introduces a compensation mechanism to mitigate gradient staleness. The introduction of FL to satellite networks has brought many new opportunities. For example, [PERSON] et al. [20] proposed a new FL-assisted distributed intrusion detection system using a mesh satellite net to protect autonomous vehicles. [PERSON] et al. [21] developed a FL module for multi-satellite, multi-modality in-orbit data fusion, which compressed communication costs by a factor of 4 and significantly reduced the training time by 48.4 min (15.18%). [PERSON] et al. [22] proposed a novel threat detection FL model for proactively identifying intrusions in satellite communication networks. This model utilizes decentralized on-device data while preserving data privacy. However, the applications of FL in satellite networks still face many unique challenges. The mobility of satellites usually makes satellite communication unstable, which relies on the predictability of satellite visits to create a system design [23]. One limitation of LEO satellites is their brief visibility period with GSs. While their orbit period typically ranges from 90 to 120 min, they are only in direct contact with a GS for 5 to 20 min per orbit [19]. Moreover, failures from the network, hardware, software, etc., make SFL face more challenges than terrestrial FL [12]. It is necessary for FL to make a client selection (CS), and this problem in terrestrial networks has been fully studied [24]. However, to the best of our knowledge, this was lacking in satellite networks. This problem needs to consider the orbital characteristics of the client satellites, data value, computing capabilities, etc., to improve the performance of the model in satellite federated learning (SFL). In this paper, we firstly investigate the characteristics of satellites links, and discuss the positioning of a parameter server (PS) in two distinct scenarios: deployment at a GS and deployment on an LEO satellite.