Low Earth Orbit (LEO) satellites play a crucial role in the development of 6G mobile networks and space-air-ground integrated systems. Recent advancements in space technology have empowered LEO satellites with the capability to run AI applications. However, centralized approaches, where ground stations (GSs) act as servers and satellites as clients, often encounter slow convergence and inefficiencies due to intermittent connectivity between satellites and GSs. In contrast, decentralized federated learning (DFL) offers a promising alternative by facilitating direct communication between satellites (clients) via inter-satellite links (ISLs). However, inter-plane ISLs connecting satellites from different orbital planes are dynamic due to Doppler shifts and pointing limitations. This could impact model propagation and lead to slower convergence. To mitigate these issues, we propose DFedSat, a fully decentralized federated learning framework tailored for LEO satellites. DFedSat accelerates the training process by employing two adaptive mechanisms for intra-plane and inter-plane model aggregation, respectively. Furthermore, a self-compensation mechanism is integrated to enhance the robustness of inter-plane ISLs against transmission failure. Additionally, we derive the sublinear convergence rate for the non-convex case of DFedSat. Extensive experimental results demonstrate DFedSat's superiority over other DFL baselines regarding convergence rate, communication efficiency, and resilience to unreliable links.

翻译：低地球轨道（LEO）卫星在6G移动网络和空天地一体化系统的发展中扮演着关键角色。空间技术的最新进展使得LEO卫星具备运行人工智能应用的能力。然而，以地面站（GS）作为服务器、卫星作为客户端的集中式方法，常因卫星与地面站间的间歇性连接而导致收敛缓慢和效率低下。相比之下，分散式联邦学习（DFL）通过星间链路（ISL）促进卫星（客户端）间的直接通信，提供了一种有前景的替代方案。然而，连接不同轨道面卫星的轨道面间ISL由于多普勒频移和指向限制而具有动态性，这可能影响模型传播并导致收敛速度变慢。为缓解这些问题，我们提出了DFedSat，一个专为LEO卫星设计的完全分散式联邦学习框架。DFedSat通过分别采用两种自适应机制进行轨道面内与轨道面间的模型聚合，从而加速训练过程。此外，框架集成了自补偿机制以增强轨道面间ISL对抗传输失败的鲁棒性。同时，我们推导了DFedSat在非凸情况下的次线性收敛速率。大量实验结果表明，在收敛速度、通信效率以及对不可靠链路的容错性方面，DFedSat均优于其他DFL基线方法。