# Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network

arXiv: https://arxiv.org/abs/2212.01215 · HTML: https://mlsanity.com/paper/2212.01215/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers](https://mlsanity.com/paper/2010.05958/) (2020)
- [Gossip training for deep learning](https://mlsanity.com/paper/1611.09726/) (2016)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms](https://mlsanity.com/paper/2007.12004/) (2020)
- [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018)
- [TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture](https://mlsanity.com/paper/2012.03214/) (2020)
- [UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks](https://mlsanity.com/paper/2003.10737/) (2020)
- [EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles](https://mlsanity.com/paper/2101.06056/) (2021)

## Built on by (most cited first, 5 of 5)

- [Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM](https://mlsanity.com/paper/2401.00685/) (2024)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks](https://mlsanity.com/paper/2412.17231/) (2024)
- [Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2505.09106/) (2025)
- [Hierarchical Learning and Computing over Space-Ground Integrated Networks](https://mlsanity.com/paper/2408.14116/) (2024)

## Abstract (arXiv)

The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services. However, training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. To tackle these challenges, we first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL). Specifically, the IoRT devices in the ground layer leverage their private data to perform model training locally, while the air nodes in the air layer and the ring-structured low earth orbit (LEO) satellite constellation in the space layer are in charge of model aggregation (synchronization) at different scales.To further enhance communication efficiency and inference performance of OBL, an efficient Communication and Non-IID-aware Air node-Satellite Assignment (CNASA) algorithm is designed by taking the data class distribution of the air nodes as well as their geographic locations into account. Furthermore, we extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks. We analyze the convergence of our OBL framework and conclude that the CNASA algorithm contributes to the fast convergence of the global model. Extensive experiments based on realistic datasets corroborate the superior performance of our algorithm over the benchmark policies.