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

• URL: http://arxiv.org/abs/2608.25496v1
• Date: Wed, 26 Aug 2026 08:07:43 GMT
• Title: FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
• Abstract summary: FedQoS is a federated-risk learning framework for predicting the future reliability of candidate links.<n>Each access node learns locally from its observed network logs, including radio, traffic, load, and service features.<n>FedQoS achieves clear reliability gains under mild non-IID data while remaining competitive under the more challenging severe non-IID condition.
• Score: 39.02414844333342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings [1.441946602098626]
  Federated Learning (FL) has emerged as a novel paradigm that enables collaborative training of ML models across multiple clients.<n>This study systematically reviews and evaluates a range of FL methods in the context of intrusion detection for DDoS attacks.<n>To the best of our knowledge, this is the first comparative analysis of FL algorithms for intrusion detection under realistic non-i.i.d. and unbalanced settings.
  arXiv  Detail & Related papers  (2025-09-22T14:25:32Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• QSCL-EWIL: Quantum Stochastic Contrast Learning for Enhanced WiFi-Based Indoor Localization [27.646746600449614]
  WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings.<n>Existing systems face challenges such as data variability, environmental noise, and difficulty detecting floor and building levels.<n>This paper introduces quantum contrast learning (QSCL), a novel framework grounded in rigorous theoretical foundations.
  arXiv  Detail & Related papers  (2025-01-06T09:54:16Z)
• Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks [62.87033427172205]
  Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly. A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
  arXiv  Detail & Related papers  (2024-01-19T17:16:40Z)
• DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks [2.114401279266792]
  Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Predictions in a timely manner is of high importance, especially for safety-critical applications as in the case of vehicular communications. DisTINQT is a novel multi-headed input-aware distributed learning framework for prediction.
  arXiv  Detail & Related papers  (2024-01-15T13:00:48Z)
• Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach [4.958511942948984]
  Ultra-reliable low-latency communication (URLLC) is the cornerstone for a broad range of emerging services in next-generation wireless networks. URLLC relies on the network's ability to proactively determine whether sufficient resources are available to support the URLLC traffic. New machine learning based method is proposed that builds on (deep) neural contextual bandits.
  arXiv  Detail & Related papers  (2024-01-05T20:26:08Z)
• Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks [16.930875340427765]
  We design a reinforcement learning agent to implement Predictive Quality of Service (PQoS) in vehicular networks. Our framework is able to identify the optimal level of compression to send automotive data under low latency and reliability constraints.
  arXiv  Detail & Related papers  (2023-02-22T10:35:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.