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This paper introduces FedQoS, a federated QoS-risk learning framework designed to enhance access-node selection in dynamic indoor-outdoor environments by predicting future QoS degradation. By leveraging local observations from access nodes and aggregating them to train a global QoS-risk predictor, FedQoS effectively estimates the probability of QoS failure for candidate links without centralizing user data. Simulation results indicate that FedQoS significantly reduces the QoS-failure rate compared to traditional signal-based and historical-QoS methods, achieving near-centralized performance even under challenging non-IID conditions.
Federated learning can now predict QoS failures in wireless networks, achieving near-centralized performance while preserving user data privacy.
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.
