针对量子联邦学习中客户端质量不均问题，提出双增益自适应聚合方法。
Adaptive Aggregation with Two Gains in QFL
- 通过几何增益与服务质量增益联合调节模型融合
- 利用量子态传输保真度、延迟和设备稳定性动态加权客户端
- 适用于量子边缘计算中的异构联邦学习场景
在量子赋能的异构经典网络中部署联邦学习时，由于客户端质量不均、量子态传送保真度波动、设备不稳定以及本地与全局模型间几何失配，系统性能显著下降。传统聚合规则假设欧几里得拓扑和通信可靠性一致，难以适配新兴量子联邦系统。本文提出A2G（Adaptive Aggregation with Two Gains）——一种双增益框架，通过几何增益调控模型空间的几何融合，并基于传送保真度、延迟和设备不稳定性构建服务质量增益，动态调节客户端权重，实现更鲁棒的聚合。
原文摘要 · Abstract (English)
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
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