# Communication-Efficient Learning for Satellite Constellations

arXiv: https://arxiv.org/abs/2511.20220 · HTML: https://mlsanity.com/paper/2511.20220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [Towards Efficient Communications in Federated Learning: A Contemporary Survey](https://mlsanity.com/paper/2208.01200/) (2022)
- [Local Exact-Diffusion for Decentralized Optimization and Learning](https://mlsanity.com/paper/2302.00620/) (2023)
- [Can 5th Generation Local Training Methods Support Client Sampling? Yes!](https://mlsanity.com/paper/2212.14370/) (2022)
- [Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2206.01952/) (2022)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [Enhancing Privacy in Federated Learning through Local Training](https://mlsanity.com/paper/2403.17572/) (2024)
- [Communication-Efficient Distributed Learning with Local Immediate Error Compensation](https://mlsanity.com/paper/2402.11857/) (2024)
- [Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2411.00263/) (2024)
- [Bringing Federated Learning to Space](https://mlsanity.com/paper/2511.14889/) (2025)

## Abstract (arXiv)

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.