ScholarWorks@숙명여자대학교

초록

Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNN) over federated learning (FL). It updates quantum parameters to the server. However, QFL aggregation involves quantum parameters from all local QNN models, leading to performance degradation due to outlier models. Thus, a novel fair QFL algorithm is proposed to enhance learning performance by eliminating outliers using Lyapunov optimization at the QFL server. This approach is used to take care of the trade-off between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Moreover, a fidelity/entropy-adaptive algorithm is proposed, which relies on the degree control to select outliers. This approach (i) quantifies the dissimilarity in quantum states between the target model and each local device using fidelity, and (ii) evaluates the imbalance of data classes in each local device using entropy. Experimental results demonstrate that the proposed fair QFL algorithm outperforms benchmarks.

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