FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection

2026-08-26

AI Summary

为解决动态异构室内外环境中仅靠即时无线电测量无法预测未来服务质量(QoS)下降的问题，提出FedQoS框架，通过联邦学习方法预测候选接入链路的可靠性并支持接入节点选择。

Abstract

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.

Problem

• Federated Learning
• QoS Prediction
• Access Selection
• Heterogeneous Networks
• Indoor-Outdoor Environments

Innovation

• Federated Learning
• QoS-Risk Prediction
• Dynamic Access Selection
• Heterogeneous Environments