Ground-Assisted LEO Satellite Federated Learning: Dynamic, Efficient, Distributed Learning

Fuyao Zhang, Dan Wang, Jiawen Kang, Dusit Niyato

Abstract: With the widespread deployment of Low Earth Orbit (LEO) satellites, they generate a vast amount of data. This data has been instrumental in supporting machine learning (ML) in various terrestrial services to address global challenges such as monitoring climate change and natural disasters. However, many national regulations restrict the direct transmission of satellite data to ground stations (GSs). Therefore, ground-assisted satellite federated learning (FL) has emerged as a paradigm to safeguard data privacy by transferring model parameters instead of raw data for collaborative training. At present, the existing ground-assisted satellite FL methods encounter practical challenges: 1) The dynamic environment of LEO satellites results in continuous changes in the types of data collected by satellites, making it difficult for traditional FL models to adapt to these changes. This can lead to a deterioration in model accuracy over extended periods of model training. 2) Communication between satellites and GS is affected by atmospheric interference and weather factors, resulting in increased transmission delays and affecting the real-time efficiency of the FL system. In response to these challenges, we propose a dynamic, efficient, and distributed ground-assisted LEO satellite federated learning (DEDFL) framework to improve model accuracy and reduce satellite communication delays. In DEDFL, we design a Balanced Class Memory Extraction and an information playback strategy that enables the onboard FL model to adapt to changing satellite data types, thus achieving a performance balance across different classes. Additionally, we propose an adaptive fine coding method for parameter adoption prior to satellite transmission, effectively reducing the delay caused by satellites and ground-specific environmental variations. Experimental results demonstrate that the DEDFL method offers better accuracy and communication efficiency than other baseline algorithms.

External IDs:dblp:journals/tmc/ZhangWKN25