# Satellite Federated Edge Learning: Architecture Design and Convergence Analysis arXiv: https://arxiv.org/abs/2404.01875 ยท HTML: https://mlsanity.com/paper/2404.01875/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 66 references that are in the map. - First posted on arXiv 2 Apr 2024. ## Builds on (most cited first, 30 of 30) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018) - [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017) - [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016) - [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020) - [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018) - [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018) - [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018) - [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021) - [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021) - [Federated Learning via Intelligent Reflecting Surface](https://mlsanity.com/paper/2011.05051/) (2020) - [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023) - [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021) - [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022) - [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020) - [Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD](https://mlsanity.com/paper/2010.12998/) (2020) - [On-Board Federated Learning for Dense LEO Constellations](https://mlsanity.com/paper/2111.12769/) (2021) - [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022) - [AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms](https://mlsanity.com/paper/2212.11522/) (2022) - [Fast model inference and training on-board of Satellites](https://mlsanity.com/paper/2307.08700/) (2023) - [Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling](https://mlsanity.com/paper/2302.13447/) (2023) - [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023) - [Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2206.01952/) (2022) - [Future Space Networks: Toward the Next Giant Leap for Humankind](https://mlsanity.com/paper/2212.05668/) (2022) - [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023) - [GPU4S: Embedded GPUs in Space -- Latest Project Updates](https://mlsanity.com/paper/2109.11074/) (2021) - [Selected Trends in Artificial Intelligence for Space Applications](https://mlsanity.com/paper/2212.06662/) (2022) - [Creating a Safety Assurance Case for an ML Satellite-Based Wildfire Detection and Alert System](https://mlsanity.com/paper/2211.04530/) (2022) - [Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approach](https://mlsanity.com/paper/2211.09672/) (2022) ## Built on by (most cited first, 10 of 10) - [Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies](https://mlsanity.com/paper/2504.01676/) (2025) - [Energy-Aware Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2409.14832/) (2024) - [Hierarchical Learning and Computing over Space-Ground Integrated Networks](https://mlsanity.com/paper/2408.14116/) (2024) - [Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning](https://mlsanity.com/paper/2501.06244/) (2025) - [Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks](https://mlsanity.com/paper/2501.15995/) (2025) - [Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks](https://mlsanity.com/paper/2504.10403/) (2025) - [Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems](https://mlsanity.com/paper/2509.12222/) (2025) - [Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning](https://mlsanity.com/paper/2603.10763/) (2026) - [Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?](https://mlsanity.com/paper/2604.19399/) (2026) - [ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal](https://mlsanity.com/paper/2608.24073/) (2026) ## Abstract (arXiv) The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.