Jammer classification with Federated Learning

TL;DR

This paper explores federated learning for classifying GNSS jamming signals, enabling privacy-preserving, effective detection of six jammer types without centralized data sharing.

Contribution

It demonstrates the feasibility of using federated learning to classify jamming signals locally on devices, maintaining privacy while achieving accuracy comparable to centralized methods.

Findings

Six jammer types effectively classified with federated learning.

Federated approach achieves similar accuracy to centralized models.

Reduces data communication and privacy risks.

Abstract

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.