  The last mile connection is dominated by wireless links where heterogeneous
nodes share the limited and already crowded electromagnetic spectrum. Current
contention based decentralized wireless access system is reactive in nature to
mitigate the interference. In this paper, we propose to use neural networks to
learn and predict spectrum availability in a collaborative manner such that its
availability can be predicted with a high accuracy to maximize wireless access
and minimize interference between simultaneous links. Edge nodes have a wide
range of sensing and computation capabilities, while often using different
operator networks, who might be reluctant to share their models. Hence, we
introduce a peer to peer Federated Learning model, where a local model is
trained based on the sensing results of each node and shared among its peers to
create a global model. The need for a base station or access point to act as
centralized parameter server is replaced by empowering the edge nodes as
aggregators of the local models and minimizing the communication overhead for
model transmission. We generate wireless channel access data, which is used to
train the local models. Simulation results for both local and global models
show over 95% accuracy in predicting channel opportunities in various network
topology.
