arXiv:2608. 03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale.
By Ziwu Liu, In\^es Pinto Gouveia, Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker
arXiv:2605.19892v2 Announce Type: replace-cross
Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresp...
By Jonas Weiss, Patricia Sagmeister, Gabriel Maiolini Capez, Dinesh Verma, Roberto Garello, Alberto Perotti, Dawid Lazaj, Alicja Musial, Jakub Nalepa, Thomas Morf, Martin Schmatz, Marek Krawczyk, Mateusz Przeliorz, Kevin Roche, Sagar Tayal, Mahalakshmi Lakshminarayanan, Nicolas Long\'ep\'e, Pierre-Philippe Mathieu, Agata Wijata
The paper introduces heterogeneous space data centers (SDCs) that combine large and small nodes to deliver AI‑centric computing and communication services for ground, aerial, and in‑orbit users. It outlines use cases, node sizing, placement strategies, and interconnection topologies, and focuses on dawn‑dusk low‑Earth orbit configurations to analyze solar power, visibility windows, and latency‑aware coverage. The authors evaluate latency, service reliability, and energy requirements for various SDC roles and deployment scales, highlighting trade‑offs among node size, orbit selection, and service portfolio, and offer design guidance and future research directions for AI infrastructure in space.
By Peng Hu, Mustafa Alhassan, Amjad Iqbal
arXiv:2606. 29324v1 Announce Type: new Abstract: Low-Earth orbit (LEO) satellite Internet has become an indispensable infrastructure that provide growing coverage for global users.
By Xiang Shi, Yifei Zhang, Peng Hu
RAMA is a retrieval‑augmented multi‑agent deep reinforcement learning framework for routing in Low‑Earth Orbit satellite networks. It combines each satellite’s local observation, recent routing history, neighboring state information, and retrieved entries from a per‑satellite episodic memory, integrating them via a Transformer‑based decision network trained with Approximate Information State objectives. In a 140‑satellite Kepler constellation, RAMA cuts mean end‑to‑end latency by 13.4% and 99th‑percentile latency by 66.7% compared to a state‑of‑the‑art DDQN, and on a 1,584‑satellite Starlink shell 1 it achieves a 93.2% mean latency reduction, with further gains from online updates.
By Xin Ming Li, Peng Hu
arXiv:2604. 25018v2 Announce Type: replace-cross Abstract: The Internet of Everything (IoE) represents an evolution of the Internet of Things (IoT) by integrating people, data, processes, and things into a unified intelligent ecosystem.
By Driss Choukri, Essaid Sabir, Elmahdi Driouch, Abdelkrim Haqiq