# Towards Client Selection in Satellite Federated Learning
## 6 Conclusions  
Federated learning (FL) is a communication-efficient machine learning framework that is well-suited for satellite applications, where communication capabilities are often limited. Additionally, satellite data transmission via wireless broadcasting inherently poses privacy and security risks. FL, however, has strong privacy protection capabilities, which can help to ensure the privacy of remote sensing data. This study investigates the positioning of parameter servers (PSs) and the problem of client selection (CS) within the context of satellite federated learning (SFL). We examined and contrasted the access times and duration from client satellites to both the ground station (GS) and the server satellite. It has been observed that a satellite, when functioning as a PS, possesses a better ability to receive client parameters within its field of view compared to a GS. Both the communication and mobility models for SFL were demonstrated. The CS is modeled as a 0-1 knapsack problem to be solved. A comparative analysis with two benchmark methods, FedSat and FedSpace, was conducted to establish the advancements of inter-satellite FL. Then, Fashion-MINIST and CIFAR-100 were applied as datasets to test the models. Notably, the experimental results indicate that the CS mechanism can expedite the convergence speed of SFL up to 12 h, and it achieved an accuracy surpassing 80%. We have not yet conducted experiments in a larger-scale constellation. In fact, future constellations may expand to the scale of tens of thousands of low Earth orbit (LEO) satellites, such as Starlink ([[https://www.starlink.com/](https://www.starlink.com/)]([https://www.starlink.com/](https://www.starlink.com/)), accessed on 1 December 2023). Therefore, the CS mechanism proposed in this paper may bring about very high computational overheads in the large-scale constellation scenario. This is a problem worth studying in future work. At the same time, we used a general dataset in the simulation. In the real satellite scenario, the remote sensing data of satellites may be different. The impact of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Scenario** & **Time for Convergence** & **Accuracy** \\ \hline FedSat & Satellites-to-GS & \(>\)48 h & 38.4 \\ FedSat + CS & Satellites-to-GS & 36 h & 80.5 \\ FedSat & Satellites-to-satellite & \(>\)48 h & 71.2 \\ FedSat + CS & Satellites-to-satellite & 20 h & 79.8 \\ \hline FedSat & Satellites-to-GS & \(>\)48 h & 37.5 \\ FedSpace + CS & Satellites-to-GS & 24 h & 85.8 \\ FedSat & Satellites-to-satellite & 48 h & 72.1 \\ FedSpace + CS & Satellites-to-satellite & 16 h & 85.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results (Fashion-MINIST). Figure 11: The result of experiment in inter-satellites scenario with CIFAR-100. long-tail distribution of data on SFL and client selection has not yet been considered. In the future, we will explore more diverse paradigms of SFL for applications, as well as consider more comprehensive CS mechanisms. Conceptualization, C.W. and Z.Y. ; methodology, C.G. ; software, C.W. and S.H. ; validation, C.W. and Z.Y. ; formal analysis, C.G. ; investigation, C.W. and S.H. ; resources, Z.Y. and C.G. ; data curation, C.W. ; writing--original draft preparation, C.W. and S.H. ; writing--review and editing, C.G. ; visualization, C.W. and S.H. ; supervision, C.G. ; project administration, C.W. ; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the Shanghai Rising-Star Program under Grant No. 19 QA140800, the Youth Innovation Promotion Association for CAS under Grant No. 2019292, and the Hong Kong, Macau, and Taiwan Science and Technology Cooperation Project in the \"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission\" under Grant No. 23510760200. Not applicable. The data presented in this study are available on request from the corresponding author.