SOURCE-LINKED INTELLIGENCE
FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
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 featu
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T08:07:43.000Z
First collected: 2026-09-21T09:22:01.459Z. This is not the publication date.