BriefGPT.xyz
Jul, 2022
可裁剪的量子联邦学习
Slimmable Quantum Federated Learning
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Won Joon Yun, Jae Pyoung Kim, Soyi Jung, Jihong Park, Mehdi Bennis...
TL;DR
本文提出SlimQFL（一种动态的QFL框架），其利用QNN的角参数和极参数可以分别训练和动态利用的特性，在时间变化的通信渠道和计算能力受限的情况下，相比传统的QFL方法，SlimQFL能够实现更高的分类准确性。
Abstract
quantum federated learning
(QFL) has recently received increasing attention, where
quantum neural networks
(QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we pr
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