Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
This addresses quantum noise issues in distributed quantum systems for QFL applications, representing an incremental improvement over existing methods.
The paper tackles quantum noise heterogeneity in Quantum Federated Learning (QFL) by proposing SpoQFL, a framework that uses sporadic learning to dynamically adjust training strategies based on noise fluctuations. Experiments on real-world datasets show it significantly outperforms conventional QFL approaches with superior training performance and more stable convergence.
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.