  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. 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 (DNN)
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 DNN for signal identification. In distributed federated
learning, each sensor broadcasts its trained model to its neighbors, collects
the DNN 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 DNN 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. Then, 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 its robustness to wireless
network effects, thereby sustaining high accuracy with low communication
overhead and energy consumption.
