  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.
