Communication-Efficient Learning for Satellite Constellations
This addresses the challenge of efficient data processing in satellite networks for applications like Earth imaging and communications, representing an incremental improvement in federated learning methods.
The paper tackles the problem of communication-efficient learning for satellite constellations by proposing a novel federated algorithm that reduces communication frequency and size while maintaining accuracy, achieving superior performance in simulations with a realistic space scenario.
Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.