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NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. For that purpose, a network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a deep neural network for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the deep neural network models from its neighbors, and aggregates them to initialize its own model for the next round of training. Without exchanging any spectrum data, this process is repeated over time such that a common deep neural network is built across the network while preserving the privacy associated with signals collected at different locations. Signal classification accuracy and convergence time are evaluated for different network topologies (including line, star, ring, grid, and random networks) and packet loss events. In addition, the reduction of communication overhead and energy consumption is considered with random participation of sensors in model updates. The results show the feasibility of extending cooperative spectrum sensing over a general multi-hop wireless network through federated learning and indicate the robustness of federated learning to wireless network effects, thereby sustaining high accuracy with low communication overhead and energy consumption.
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