Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low communication overhead, Federated Averaging (FedAvg) is the standard aggregation choice in QFL literature. However, deploying QFL on practical hardware exposes a severe double-drift phenomenon: the global model is simultaneously derailed by client drift from non-IID data and hardware bias from noisy quantum gradient estimates. In this work, we first analyze the convergence of FedAvg under these realistic conditions, mathematically demonstrating that quantum hardware bias creates a persistent error floor that standard averaging cannot correct. To overcome this limitation, we propose Q-ANCHOR, a quantum-aware federated aggregation architecture that anchors server updates with zero-noise extrapolation while applying stateful client correction to suppress both client drift and hardware-induced bias. Our convergence theory proves that Q-ANCHOR successfully mitigates classical client drift while actively reducing the hardware-bias floor. Experimental results demonstrate that Q-ANCHOR achieves significantly more stable training than conventional FL baselines.

翻译：量子联邦学习提供了一种有前景的框架，可在保持数据严格本地化的同时，跨分布式客户端训练量子模型。由于其简便性和低通信开销，联邦平均是量子联邦学习文献中的标准聚合选择。然而，在实际硬件上部署量子联邦学习暴露出严重的双重漂移现象：全局模型同时受到来自非独立同分布数据的客户端漂移和来自含噪声量子梯度估计的硬件偏差的干扰。在本工作中，我们首先分析了这些现实条件下联邦平均的收敛性，从数学上证明量子硬件偏差会创建一个标准平均无法修正的持续误差底限。为克服这一限制，我们提出Q-ANCHOR，一种量子感知的联邦聚合架构，该架构通过零噪声外推锚定服务器更新，同时应用有状态的客户端修正来抑制客户端漂移和硬件引起的偏差。我们的收敛理论证明，Q-ANCHOR成功缓解了经典客户端漂移，同时主动降低了硬件偏差底限。实验结果表明，与传统的联邦学习基线相比，Q-ANCHOR实现了显著更稳定的训练。