Learning in Orbit: A Physics-Aware Graph-Decentralized Federated Learning for Multi-Satellite Space Situational Awareness

Gagandeep Kaur, Ranjitha Prasad

Keywords: Federated learning, graph decentralized federated learnig, physics informed neural networks, quantization, space situational awareness, deinterleaving, light cone, cayley menger determinant, two body dynamics

TL;DR: Serverless, physics-aware Graph-DFL for SSA: LEO GRU deinterleaving + PINN orbital modeling with CM×LC-weighted, 4-bit quantized neighbor updates; achieves privacy-preserving, robust convergence and low RMSE on real TLE/SGP4 data.

Abstract: The orbital environment is increasingly congested, heightening collision risk and demanding robust Space Situational Awareness (SSA). Ground-based tracking and centralized learning face latency, fragmented datasets, and strict privacy limits on telemetry sharing. While ML aids orbital prediction, purely data-driven models fail under sparse or irregular observations; Physics-Informed Neural Networks (PINNs) embed dynamics for physical consistency. However, locally trained PINNs lack shared context, fragmenting awareness. Collaboration is often constrained by privacy policy, export controls, or mission secrecy—sometimes forcing purely local learning and leaving blind spots. This motivates Federated Learning (FL), where satellites share model updates (not raw data) to refine physics-consistent predictors while preserving data autonomy. However, single-server FL is ill-suited for orbital networks, as it creates a single point of failure, over-smooths data, and exposes vulnerability to link outages. We therefore propose Graph-Decentralized Federated Learning (Graph-DFL) for multi-satellite SSA: a serverless framework where satellites exchange quantized incremental updates only with neighbors and reach consensus via topology-aware diffusion. Each Low Earth Orbit (LEO) client trains a GRU-based deinterleaver and a local PINN, while Medium Earth Orbit (MEO) relays apply confidence-weighted Cayley–Menger × Light-Cone (CM × LC) fusion. Experiments on real Two-Line Element (TLE)–derived SGP4 trajectories show that Graph-DFL achieves high deinterleaver accuracy and low trajectory RMSE, indicating a resilient, physics-consistent, privacy-preserving solution for SSA without centralization.

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Submission Number: 17