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NextG networks are intended to provide the flexibility of sharing thespectrum with incumbent users and support various spectrum monitoring taskssuch as anomaly detection, fault diagnostics, user equipment identification,and authentication. A network of wireless sensors is needed to monitor thespectrum for signal transmissions of interest over a large deployment area.Each sensor receives signals under a specific channel condition depending onits location and trains an individual model of a deep neural network (DNN)accordingly to classify signals. To improve the accuracy, individual sensorsmay exchange sensing data or sensor results with each other or with a fusioncenter (such as in cooperative spectrum sensing). In this paper, distributedfederated learning over a multi-hop wireless network is considered tocollectively train a DNN for signal identification. In distributed federatedlearning, each sensor broadcasts its trained model to its neighbors, collectsthe DNN models from its neighbors, and aggregates them to initialize its ownmodel for the next round of training. Without exchanging any spectrum data,this process is repeated over time such that a common DNN is built across thenetwork while preserving the privacy associated with signals collected atdifferent locations. Signal classification accuracy and convergence time areevaluated for different network topologies (including line, star, ring, grid,and random networks) and packet loss events. Then, the reduction ofcommunication overhead and energy consumption is considered with randomparticipation of sensors in model updates. The results show the feasibility ofextending cooperative spectrum sensing over a general multi-hop wirelessnetwork through federated learning and indicate its robustness to wirelessnetwork effects, thereby sustaining high accuracy with low communicationoverhead and energy consumption.

Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks