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.

翻译：干扰信号可能危及全球导航卫星系统接收机的运行，甚至导致其完全失效。鉴于此类信号的普遍性，干扰抑制与定位技术具有至关重要的意义，而干扰分类技术对此具有辅助作用。数据驱动模型已被证实能有效检测这些威胁，但在利用众包数据训练模型时，私人数据共享仍构成挑战。本文探讨了利用联邦学习在每台设备上本地训练干扰信号分类器的方法，将模型更新在中央服务器进行聚合与平均处理。这种方案可实现隐私保护的训练流程，无需集中式数据存储或访问客户端本地数据。采用FedAvg框架在包含模拟干扰全球导航卫星系统信号的频谱图数据集上进行评估。六种不同类型的干扰信号均得到有效分类，其结果与需要海量数据通信且存在隐私保护问题的全集中式方案相比具有可比性。