Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization

• URL: http://arxiv.org/abs/2509.24932v2
• Date: Thu, 02 Oct 2025 21:56:54 GMT
• Title: Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization
• Authors: Fardis Nadimi, Payam Abdisarabshali, Jacob Chakareski, Nicholas Mastronarde, Seyyedali Hosseinalipour,
• Abstract summary: We introduce Fed-Span, a novel distributed learning framework for satellite networks.<n>Fed-Span aims to address challenges inherent to distributed learning in satellite networks.<n>We show that Fed-Span outperforms existing methods, with faster model convergence, greater energy efficiency, and reduced latency.
• Score: 12.126031297041235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Fed-Span, a novel federated/distributed learning framework designed for low Earth orbit satellite constellations. Fed-Span aims to address critical challenges inherent to distributed learning in dynamic satellite networks, including intermittent satellite connectivity, heterogeneous computational capabilities of satellites, and time-varying satellites' datasets. At its core, Fed-Span leverages minimum spanning tree (MST) and minimum spanning forest (MSF) topologies to introduce spanning model aggregation and dispatching processes for distributed learning. To formalize Fed-Span, we offer a fresh perspective on MST/MSF topologies by formulating them through a set of continuous constraint representations (CCRs), thereby devising graph-theoretical abstractions into an optimizable framework for satellite networks. Using these CCRs, we obtain the energy consumption and latency of operations in Fed-Span. Moreover, we derive novel convergence bounds for Fed-Span, accommodating its key system characteristics and degrees of freedom (i.e., tunable parameters). Finally, we propose a comprehensive optimization problem that jointly minimizes model prediction loss, energy consumption, and latency of Fed-Span. We unveil that this problem is NP-hard and develop a systematic approach to transform it into a geometric programming formulation, solved via successive convex optimization with performance guarantees. Through evaluations on real-world datasets, we demonstrate that Fed-Span outperforms existing methods, with faster model convergence, greater energy efficiency, and reduced latency. These results highlight Fed-Span as a novel solution for efficient distributed learning in satellite networks.

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