Jammer classification with Federated Learning
This addresses privacy concerns in crowdsourced data for jamming mitigation in GNSS systems, though it is incremental as it applies an existing federated learning method to a new domain.
The paper tackles the problem of classifying jamming signals in GNSS receivers using federated learning to preserve privacy, achieving comparable classification results to centralized methods for six jammer types.
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation. Given their ubiquity, jamming mitigation and localization techniques are of crucial importance, for which jammer classification is of help. Data-driven models have been proven useful in detecting these threats, while their training using crowdsourced data still poses challenges when it comes to private data sharing. This article investigates the use of federated learning to train jamming signal classifiers locally on each device, with model updates aggregated and averaged at the central server. This allows for privacy-preserving training procedures that do not require centralized data storage or access to client local data. The used framework FedAvg is assessed on a dataset consisting of spectrogram images of simulated interfered GNSS signal. Six different jammer types are effectively classified with comparable results to a fully centralized solution that requires vast amounts of data communication and involves privacy-preserving concerns.