Whitening-enhanced and participation-aware over-the-air computation for stable vehicular federated learning
TL;DR
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.
Abstract
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.