FedQoS：面向异构室内外接入选择的联邦QoS风险学习
FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
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中文总结 AI 辅助
FedQoS是一种联邦QoS风险学习框架，通过各接入节点本地学习并联邦聚合训练全局预测器，在无需集中用户数据的情况下，降低动态异构室内外环境的QoS故障率，实现可靠接入选择。
中文摘要 AI 辅助
在动态且异构的室内外环境中，可靠的接入选择颇具挑战，因为仅瞬时无线电测量无法捕捉由移动性、遮挡、流量负载和资源竞争引发的未来QoS下降。本文提出FedQoS，这是一种联邦QoS风险学习框架，用于预测候选接入链路的未来可靠性，支持接入节点选择，且无需集中用户级网络数据。在FedQoS中，每个接入节点从其观测到的网络日志中进行本地学习，日志包含无线电、流量、负载和服务上下文特征，同时通过联邦聚合训练全局QoS风险预测器。所学模型估计每个候选链路的QoS故障概率，控制器利用这些风险评分在动态网络条件下选择可靠的接入节点。为评估该框架，我们使用Sionna框架构建基于物理的合成室内外无线数据集，涵盖正常流量、移动性、事件驱动拥塞和非IID客户端观测。仿真结果表明，与基于信号和基于历史QoS的启发式方法相比，基于学习的接入选择大幅降低了QoS故障率。FedQoS实现了接近集中式的预测性能，在轻度非IID数据下提供了显著的可靠性提升，在更具挑战性的严重非IID条件下仍具竞争力。这些结果证明了联邦QoS风险学习在动态无线环境中实现可靠、数据本地化接入选择的潜力。
英文摘要
Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobility, blockage, traffic load, and resource competition. This paper proposes FedQoS, a federated QoS-risk learning framework for predicting the future reliability of candidate access links and supporting access-node selection without centralizing user-level network data. In FedQoS, each access node locally learns from its observed network logs, including radio, traffic, load, and service-context features, while a global QoS-risk predictor is trained through federated aggregation. The learned model estimates the probability of QoS failure for each candidate link, and the controller uses these risk scores to select reliable access nodes under dynamic network conditions. To evaluate the framework, we construct physics-based synthetic indoor-outdoor wireless datasets using the Sionna framework, covering normal traffic, mobility, event-driven congestion, and non-IID client observations. Simulation results show that learning-based access selection substantially reduces the QoS-failure rate compared with signal-based and historical-QoS heuristic methods. FedQoS achieves near-centralized predictive performance and provides clear reliability gains under mild non-IID data while remaining competitive under the more challenging severe non-IID condition. These results demonstrate the potential of federated QoS-risk learning for reliable, data-local access selection in dynamic wireless environments.
发表机构
- Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学跨学科安全、可靠性与信任中心（SnT））
机构由 AI 辅助整理，请以论文原文为准。
