Slimmable Quantum Federated Learning
This work addresses efficiency and robustness issues in quantum federated learning for applications in distributed quantum computing, though it appears incremental as it builds on existing QFL methods.
The authors tackled the problem of static quantum federated learning by proposing SlimQFL, a dynamic framework that adapts to time-varying communication channels and computing energy limitations, achieving higher classification accuracy than Vanilla QFL, especially under poor channel conditions.
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.