  In this paper, we propose a framework for predicting frame errors in the
collaborative spectrally congested wireless environments of the DARPA Spectrum
Collaboration Challenge (SC2) via a recently collected dataset. We employ
distributed deep edge learning that is shared among edge nodes and a central
cloud. Using this close-to-practice dataset, we find that widely used federated
learning approaches, specially those that are privacy preserving, are worse
than local training for a wide range of settings. We hence utilize the
synthetic minority oversampling technique to maintain privacy via avoiding the
transfer of local data to the cloud, and utilize knowledge distillation with an
aim to benefit from high cloud computing and storage capabilities. The proposed
framework achieves overall better performance than both local and federated
training approaches, while being robust against catastrophic failures as well
as challenging channel conditions that result in high frame error rates.
