Performance analysis of federated learning in orbital edge computing

Federated Learning (FL) is a promising solution for collaborative machine learning while respecting data privacy and locality. FL has been used in Low Earth Orbit (LEO) satellite constellations for different space applications including earth observation, navigation, and positioning. Orbital Edge Computing (OEC) refers to the deployment of edge computing resources and data processing capabilities in space-based systems, enabling real-time data analysis and decision-making for remote and space-based applications. While there is existing research exploring the integration of federated learning in OEC, the influence of diverse factors such as space conditions, communication constraints, and machine learning models remains uncertain. This paper addresses this gap and presents a comprehensive performance analysis of FL methods in the unique and challenging setting of OEC. We consider model accuracy, training time, and power consumption as the performance metrics under different working conditions including IID and non-IID data distributions to analyse the performance of centralised and decentralised FL approaches. The experimental results demonstrate that although the asynchronous centralised FL method has high fluctuations in the accuracy curve, it is suitable for space applications in which power consumption and training time are two main factors. In addition, the number of sampled satellites for decentralised FL methods is an important parameter in non-IID data distribution. Moreover, increasing altitude can reduce the training time and increase the power consumption. This study enables us to highlight a number of performance challenges in OEC for further investigation.