Olive Branch Learning: A Topology-Aware Federated Learning Framework for
Space-Air-Ground Integrated Network
- URL: http://arxiv.org/abs/2212.01215v1
- Date: Fri, 2 Dec 2022 14:51:42 GMT
- Title: Olive Branch Learning: A Topology-Aware Federated Learning Framework for
Space-Air-Ground Integrated Network
- Authors: Qingze Fang and Zhiwei Zhai and Shuai Yu and Qiong Wu and Xiaowen Gong
and Xu Chen
- Abstract summary: Training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues.
We first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL)
We extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks.
- Score: 19.059950250921926
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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