Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation
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Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning...
The paper introduces a heterogeneous federated learning approach using the FractalNet architecture tailored for satellite mega‑constellations. It formalizes contact‑window‑constrained, depth‑heterogeneous optimization and proposes a distributed path scheduler that assigns model depth based on satellite SWAP‑C constraints, predicted contacts, and training statistics. The framework includes periodic update pooling and a three‑tier agentic control plane, and is validated through a wildfire detection case study across LEO, MEO, and GEO/HEO shells, demonstrating improvements in convergence, communication efficiency, energy adaptation, and robustness.
arXiv:2608.24516v1 Announce Type: cross Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, there...
The paper introduces ALMOND, a cognitive framework that bridges the gap between human intent and low‑level control logic for 6G low Earth orbit satellite networks. It employs a bidirectional semantic engine to convert natural‑language requests into multi‑objective reward functions and extracts operational constraints from failed reinforcement‑learning trajectories, while a large‑language‑model‑guided graph neuroevolution designs efficient policy architectures. In a 350‑satellite digital twin, ALMOND improves polar handover success from 55% to over 95%, speeds convergence threefold, and cuts reward‑engineering time from 48 to 4.5 hours, all within a compute‑frugal on‑board policy of under 50,000 parameters.
Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground se...
arXiv:2601. 06188v3 Announce Type: replace Abstract: As Earth-observing satellite constellations grow in size and capability, distributed onboard control offers a pathway to novel responses and time-sensitive measurements.