Federated Learning of Jamming Classifiers: From Global to Personalized Models

Jamming signals can jeopardize and ultimately prevent the effective operation of global navigation satellite system (GNSS) receivers. Given the ubiquity of these signals, jamming mitigation and localization techniques are of crucial importance, and these techniques can be enhanced with accurate jammer classification methods. Although data-driven models have proven useful for detecting jamming signals, training these models using crowdsourced data requires sharing private data and may therefore compromise user privacy. This article explores the use of federated learning to locally train jamming signal classifiers on each device, with model updates aggregated and averaged at a central server. This approach ensures user privacy during model training by removing the need for centralized data storage or access to clients’ local data. The personalized federated learning strategies employed in this study are also tested on non-independent and identically distributed data sets composed of spectrogram images from interfered GNSS signals. In addition, this article discusses the effect of model quantization, which is used to effectively reduce communication costs, as well as a fusion strategy for personalized federated learning schemes in which multiple classifiers are available.