Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 177-182· 0 citations· 16 references
Abstract
Conventional federated learning (FL) methods face critical challenges in realistic wireless edge networks, where training data are often limited and heterogeneous, which can lead to unstable training and poor generalization. To address these challenges in a principled manner, we propose a novel Bayesian wireless FL framework grounded in Bayesian inference. By explicitly modeling uncertainty, the proposed framework mitigates local overfitting and client drift, thereby enabling more reliable inference. Nevertheless, adopting Bayesian FL increases communication overhead due to the need to transmit richer model information and fundamentally changes the aggregation process beyond simple averaging. To overcome this limitation, we design a dedicated over-the-air computation (AirComp) scheme tailored to Bayesian FL, which efficiently aggregates local posterior distributions at the distribution level by exploiting the superposition property of wireless channels. Simulations validate the proposed framework, demonstrating significant gains in test accuracy over conventional FL methods, particularly in data-scarce and heterogeneous environments.
An inference-aware AirMoE error metric is constructed to quantify aggregation distortion effects on end-to-end (E2E) inference accuracy via perturbation-based layer-sensitivity calibration, and an activation- and channel-aware expert placement strategy is developed that assigns more important experts to devices with lo...
Federated learning (FL) applications normally employ large deep learning (DL) models, resulting in excessive communication overhead in the deployment of FL over resource-constraint mobile edge networks. To achieve better scalability for DL-based FL, we capitalize on both the asymmetric nature of mobile networks and the...
Zi-Han Chen, H. Yang, Tony Q. S. Quek et al.· IEEE Transactions on Cogniti...· 0 citations
This work proposes a participation-aware learning and communication joint design under covariance uncertainty bounded by the Frobenius norm, and introduces a specific participation coefficient for each vehicle, which functions equivalently to an adaptive central server step size for standard federated averaging.
Xiaoyu Zhang, Jia-Sheng Zhu, Lei Wang et al.· International Conference on...· 0 citations
A novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework, which employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios.
It is demonstrated that the reinforcement learning-based approaches, namely Q-learning and SARSA (State-Action-Reward-State-Action), consistently outperform random selection in terms of total channel capacity, attacker detection accuracy, and performance stability.
SigMap is a multimodal foundation model that introduces two key innovations: a cycle-adaptive masking strategy that dynamically adjusts masking patterns based on channel periodicity characteristics to learn robust wireless representations and a novel"map-as-prompt"framework that integrates 3D geographic information thr...
