Dynamic Quantum Federated Learning for Satellite-Ground Integrated Systems Using Slimmable Quantum Neural Networks
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초록

Recent advances in low Earth orbit (LEO) satellites have made it possible to achieve zero blind spots on Earth. Considering the give locations of these devices, this makes satellite-ground links between quantum devices a practical possibility. This paper proposes the first quantum federated learning (QFL) application in satellite-ground communication. To improve communication and computing performance, this paper adopts slimmable quantum federated learning (SQFL) and slimmable quantum neural networks (sQNN), which allow for two different configurations in quantum neural networks: the angle and pole configurations. This paper also employs superposition coding and successive decoding to increase communication opportunities. Through extensive experiments, the proposed satellite-ground SQFL framework performs well and is both computationally and communicationally efficient compared to classical federated learning and QFL.

키워드

Satellite-ground communicationquantum machine learningquantum computingfederated learning
제목
Dynamic Quantum Federated Learning for Satellite-Ground Integrated Systems Using Slimmable Quantum Neural Networks
저자
Park, SoohyunJung, SoyiKim, Joongheon
DOI
10.1109/ACCESS.2024.3392429
발행일
2024-04
유형
Article
저널명
IEEE Access
12
페이지
58239 ~ 58247