# FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks

arXiv: https://arxiv.org/abs/2412.17231 · HTML: https://mlsanity.com/paper/2412.17231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 60 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [FedCM: Federated Learning with Client-level Momentum](https://mlsanity.com/paper/2106.10874/) (2021)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [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 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)
- [Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2401.09656/) (2024)
- [Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference](https://mlsanity.com/paper/2305.05933/) (2023)
- [A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN](https://mlsanity.com/paper/2403.17400/) (2024)
- [Space-ground Fluid AI for 6G Edge Intelligence](https://mlsanity.com/paper/2411.15845/) (2024)
- [Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2212.01215/) (2022)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [End-to-End Delivery in LEO Mega-constellations and the Reordering Problem](https://mlsanity.com/paper/2405.07627/) (2024)
- [LoLaFL: Low-Latency Federated Learning via Forward-only Propagation](https://mlsanity.com/paper/2412.14668/) (2024)

## Built on by (most cited first, 2 of 2)

- [PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference](https://mlsanity.com/paper/2503.22982/) (2025)
- [Space Generative AI with Solar Energy Harvesting](https://mlsanity.com/paper/2609.01062/) (2026)

## Abstract (arXiv)

To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission of SGINs is to support federated learning (FL) at a global scale. However, existing space-ground integrated FL frameworks involve ground stations or costly inter-satellite links, entailing excessive training latency and communication costs. To overcome these limitations, we propose an infrastructure-free federated learning framework based on a model dispersal (FedMeld) strategy, which exploits periodic movement patterns and store-carry-forward capabilities of satellites to enable parameter mixing across large-scale geographical regions. We theoretically show that FedMeld leads to global model convergence and quantify the effects of round interval and mixing ratio between adjacent areas on its learning performance. Based on the theoretical results, we formulate a joint optimization problem to design the staleness control and mixing ratio (SC-MR) for minimizing the training loss. By decomposing the problem into sequential SC and MR subproblems without compromising the optimality, we derive the round interval solution in a closed form and the mixing ratio in a semi-closed form to achieve the optimal latency-accuracy tradeoff. Experiments using various datasets demonstrate that FedMeld achieves superior model accuracy while significantly reducing communication costs as compared with traditional FL schemes for SGINs.