解决量子联邦学习在非独立同分布数据下的性能下降问题
Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models
- 按客户端类别分布相似性分组，分组聚合经典特征提取器
- 量子参数采用环形均值聚合，提升全局更新稳定性
- 适合研究混合量子-经典联邦学习的学者与工程人员
联邦学习可在隐私约束下实现跨去中心化客户端的协同模型训练。量子计算有望缓解联邦学习中的计算与通信负担，但混合经典-量子联邦学习在非独立同分布（non-IID）数据下仍易出现性能下降。为此，我们提出FEDCOMPASS，一种用于混合经典-量子联邦学习的分层聚合框架。该框架利用谱聚类根据客户端类别分布相似性进行分组，并对经典特征提取器实施组内聚合；对于量子参数，采用环形均值聚合结合自适应优化，确保全局更新稳定。在三个基准数据集上的实验表明，FEDCOMPASS在非IID设置下测试准确率最高提升10.22%，并显著增强收敛稳定性，优于六种强基线联邦学习方法。
原文摘要 · Abstract (English)
Federated learning enables collaborative model training across decentralized clients under privacy constraints. Quantum computing offers potential for alleviating computational and communication burdens in federated learning, yet hybrid classical-quantum federated learning remains susceptible to performance degradation under non-IID data. To address this,we propose FEDCOMPASS, a layered aggregation framework for hybrid classical-quantum federated learning. FEDCOMPASS employs spectral clustering to group clients by class distribution similarity and performs cluster-wise aggregation for classical feature extractors. For quantum parameters, it uses circular mean aggregation combined with adaptive optimization to ensure stable global updates. Experiments on three benchmark datasets show that FEDCOMPASS improves test accuracy by up to 10.22% and enhances convergence stability under non-IID settings, outperforming six strong federated learning baselines.
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