Slimmable Quantum Federated Learning

• URL: http://arxiv.org/abs/2207.10221v1
• Date: Wed, 20 Jul 2022 22:39:23 GMT
• Title: Slimmable Quantum Federated Learning
• Authors: Won Joon Yun, Jae Pyoung Kim, Soyi Jung, Jihong Park, Mehdi Bennis, and Joongheon Kim
• Abstract summary: Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL) We propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. Simulation results corroborate that SlimQFL achieves higher classification accuracy than Vanilla QFL, particularly under poor channel conditions on average.
• Score: 44.89303833148191
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. This is made viable by leveraging the unique nature of a QNN where its angle parameters and pole parameters can be separately trained and dynamically exploited. Simulation results corroborate that SlimQFL achieves higher classification accuracy than Vanilla QFL, particularly under poor channel conditions on average.

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