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FedQoS framework uses federated learning for reliable wireless access selection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FedQoS, a federated learning framework designed to improve access selection in dynamic indoor-outdoor wireless environments. This system predicts the future reliability of access links by learning from local network data at each access node, aggregating these learnings into a global QoS-risk predictor. The framework aims to reduce Quality of Service (QoS) failure rates compared to traditional signal-based methods, demonstrating strong performance even with non-independent and identically distributed (non-IID) client observations. AI

IMPACT This research could lead to more reliable and efficient wireless network performance by leveraging federated learning for predictive access selection.

RANK_REASON This is a research paper detailing a novel framework for wireless network access selection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FedQoS framework uses federated learning for reliable wireless access selection

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31 / 100
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This is a research paper detailing a novel framework for wireless network access selection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Zerihun Huruy, Vu Nguyen Ha, Symeon Chatzinotas ·

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

    arXiv:2608.25496v1 Announce Type: new 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 r…

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