Jammer classification with Federated Learning
Peng Wu Helena Calatrava Tales Imbiriba Pau Closas
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.