mlsanityData to 7 Oct 2026

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

arXiv 2212.01215 ↗First posted 2 Dec 2022cs.NI cs.AI cs.DCcited by 5

Not on the map. Why?

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 2 Dec 2022.

Builds on

17 of 51 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2018Group Normalizationcited by 1,514
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  8. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  9. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  10. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  11. 2016Gossip training for deep learningcited by 33
  12. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
5 more
  1. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  2. 2018Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligencecited by 14
  3. 2020TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecturecited by 12
  4. 2020UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networkscited by 4
  5. 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehiclescited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  2. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  3. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  4. 2025Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Networkcited by 2
  5. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1