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 lower channel-power cost.
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 distinct effects of partial transmissions on FL training for the global and local models. We propose Fed-DynAmal, an FL framework that decreases the number of parameters transmitted in the uplink (clients-to-server) while concurrently achieving better model performance. The underlying idea is that each selected client sends a partial DL model to the server by omitting several sub-blocks from the trained local model. Crucially, we drop the assumption that transmitted local models can still be used for inference, thereby allowing for greater model variability. At the server, we introduce amalgamation, a process to merge different partial local models into an inference-viable full model. Essentially, amalgamation is a bridge for performing aggregation at the sub-block level. Interestingly, as the key takeaway, communication efficiency versus model performance is not necessarily a trade-off in FL: Our extensive experiments show that Fed-DynAmal can effectively improve communication efficiency while still concurrently achieving higher accuracy and enhanced robustness.
Zihan Chen, H. Yang, Tony Q. S. Quek et al.· IEEE Transactions on Cogniti...· 0 citations
Over-the-air computation enables instantaneous analog aggregation to alleviate uplink latency in vehicular federated learning. However, existing methods often assume perfect channel state information or exact error statistics. This assumption is impractical given the rapid channel changes and dynamic conditions typical of vehicular networks. To address this gap, we propose a participation-aware learning and communication joint design under covariance uncertainty bounded by the Frobenius norm. We introduce a specific participation coefficient for each vehicle, which functions equivalently to an adaptive central server step size for standard federated averaging. This mechanism enables stability control without altering the original algorithmic weights. Furthermore, we prove that the worst-case normalized mean-squared error under our uncertainty model admits an exact deterministic reformulation, solvable via an efficient alternating optimization algorithm. Experiments using the MNIST dataset demonstrate improved aggregation fidelity, faster convergence, and significantly reduced training volatility.
Xiaoyu Zhang, Jiasheng Zhu, Lei Wang et al.· International Conference on...· 0 citations
Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static network optimized for specific channel conditions, their generalization capability across diverse scenarios remains a significant challenge. To address this issue, this paper proposes a novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework. The proposed architecture employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios. The experts are constructed via stacked ResNet blocks and specialize in robust signal detection within specific channel conditions, while the routing mechanism incorporates a lightweight representation learning module, which projects the coarse channel estimate into a low-dimensional latent embedding. The learned embedding characterizes task-relevant channel conditions and provides efficient guidance for accurate expert selection. Link-level simulation experiments demonstrate that the proposed CARNet achieves superior performance across diverse channel conditions.
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 through lightweight soft prompts for effective cross-scenario adaptation.
