The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising concept. This paper proposes FedLEO, a novel federated learning (FL) framework that realizes the concept of SEC and overcomes the limitation (slow convergence) of existing FL-based solutions. FedLEO (1) augments the conventional FL's star topology with ``horizontal'' intra-plane communication pathways in which model propagation among satellites takes place; (2) optimally schedules communication between ``sink'' satellites and the GS by exploiting the predictability of satellite orbiting patterns. We evaluate FedLEO extensively and benchmark it with the state of the art. Our results show that FedLEO drastically expedites FL convergence, without sacrificing -- in fact it considerably increases -- the model accuracy.
Missouri University of Science and Technology（密苏里科技大学）
Low Earth Orbit (LEO) satellite constellations have seen a surge in deployment over the past few years by virtue of their ability to provide broadband Internet access as well as to collect vast amounts of Earth observational data that can be utilized to develop AI on a global scale. As traditional machine learning (ML) approaches that train a model by downloading satellite data to a ground station (GS) are not practical, Federated Learning (FL) offers a potential solution. However, existing FL approaches cannot be readily applied because of their excessively prolonged training time caused by t…
Missouri University of Science and Technology（密苏里科技大学）
Low Earth Orbit (LEO) constellations, each comprising a large number of satellites, have become a new source of big data "from the sky". Downloading such data to a ground station (GS) for big data analytics demands very high bandwidth and involves large propagation delays. Federated Learning (FL) offers a promising solution because it allows data to stay in-situ (never leaving satellites) and it only needs to transmit machine learning model parameters (trained on the satellites' data). However, the conventional, synchronous FL process can take several days to train a single FL model in the con…
National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southampton（南安普顿大学）；Hon Hai Research Institute（鸿海研究院）
Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accura…
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the case of excessive network dynamics. DTN node buffer management is still an active area of research, as the current implementation of the DTN core protocol still relies on the assumption that there is always enough memory available in different network nodes to store and forward bundles. In addition, the classical queuing theory does not apply to the dynamic man…
DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares
Research Center for Information Technology Innovation (CITI), Academia Sinica (AS)（信息科技创新研究中心（CITI），中国科学院（AS））；Department of Electrical Engineering, National Taiwan Normal University (NTNU)（电气工程系，国立台湾师范大学（NTNU））；Bradley Department of Electrical and Computer Engineering (ECE), Virginia Tech (VT)（电气与计算机工程系（ECE），弗吉尼亚理工学院（VT））
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
School of Electrical and Mechanical Engineering, University of Adelaide（阿德莱德大学电气与机械工程学院）；College of Computing and Informatics, University of Sharjah（沙迦大学计算机与信息学院）；Singapore University of Technology and Design（新加坡科技与设计大学）；Electronics and Telecommunications Research Institute（电子电信研究所）
本文提出一种可扩展的分层 SDN 框架，利用图神经网络和 Koopman 理论来优化低轨 mega-星座的网络管理，实验证明在空间压缩和时间预测方面有显著提升。
Korea University（高丽大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and a mobile high-altitude platform (HAP) such as a fixed-wing UAV. To maximize the end-to-end data rat…
Asynchronous Risk-Aware Multi-Agent Packet Routing for Ultra-Dense LEO Satellite Networks
The Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（安全、可靠性与信任跨学科研究中心（SnT），卢森堡大学）；School of Computer Sciense and Cyber Engineering, Guangzhou University（计算机科学与网络工程学院，广州大学）
