FedQoS is a federated learning framework that predicts future QoS failure probabilities for candidate access links in dynamic indoor‑outdoor environments, enabling reliable access‑node selection without centralizing user data. Each access node trains locally on its network logs, while a global QoS‑risk predictor is built through federated aggregation. Simulations using physics‑based synthetic datasets show that FedQoS reduces QoS‑failure rates compared to signal‑based and historical‑QoS heuristics, achieving near‑centralized performance even under non‑IID data conditions.
Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobilit...
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
arXiv:2605. 00457v4 Announce Type: replace-cross Abstract: The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-Fi performance degradation.
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
arXiv:2606. 00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access.
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
arXiv:2606. 10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations.
The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
arXiv:2609.14246v1 Announce Type: cross Abstract: In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further af...
arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
arXiv:2606. 09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost.
arXiv:2412.06210v3 Announce Type: replace Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use...