Jammer classification with Federated Learning

Peng Wu, Helena Calatrava, Tales Imbiriba, Pau Closas

Abstract: Abstract—Jamming signals can jeopardize the operation of GNSS receivers until deying 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. Index Terms—Jamming detection, machine learning, distributed inference, neural networks, federated learning.
Published in: 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 24 - 27, 2023
Hyatt Regency Hotel
Monterey, CA
Pages: 228 - 234
Cite this article: Wu, Peng, Calatrava, Helena, Imbiriba, Tales, Closas, Pau, "Jammer classification with Federated Learning," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 228-234. https://doi.org/10.1109/PLANS53410.2023.10140124
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